Mathematical Sciences

Errata
Item Errata Date
1.

The prerequisites have been removed for the following units:

MATH1021 Calculus Of One Variable
MATH1023 Multivariable Calculus and Modelling

12/12/2018
2.

The prerequisites have been removed and the assumed kowledge has changed for the following units:

MATH1921 Calculus Of One Variable (Advanced): Prerequisites have been removed. Assumed knowledge now reads: (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent.

MATH1931 Calculus Of One Variable (SSP): Prerequisites have been removed. Assumed knowledge  now reads: (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent.

MATH1923 Multivariable Calculus and Modelling (Adv): Prerequisites have been removed. Assumed knowledge now reads: (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent

MATH1933 Multivariable Calculus and Modelling (SSP):Prerequisites have been removed. Assumed knowledge now reads: (HSC Mathematics Extension 2) OR (Band E4 in HSC Mathematics Extension 1) or equivalent.

12/12/2018

MATHEMATICAL SCIENCES

Mathematical Sciences program

The Mathematical Sciences program is available only to students eligible to enroll in the Dalyell stream.
A program in Mathematical Sciences requires 66 credit points including:
(i) A 48 credit point major in either, Data Science, Financial Mathematics and Statistics, Mathematics or Statistics, and:
(ii) 6 credit points of 1000-level program core units according to the following rules:
(a) For students with a major in Financial Mathematics and Statistics, Mathematics or Statistics, 6 credit points of 1000-level program core (non-Data Science major) units in addition to those counted towards the major.
(b) For students with a major in Data Science, 6 credit points of 1000-level program core (Data Science major) units in addition to those counted towards the major.
(iii) 12 credit points of 2000-level and 3000-level selective units according to the following rules:
(a) For students with a major in Mathematics, 6 credit points of 2000-level program selective (Mathematics major) units and 6 credit points of 3000- or 4000-level selective units, in addition to those counted towards the major.
(b) For students with a major in Statistics, 6 credit points of 2000- level program selective set one (Statistics major) units, and 6 credit points of 2000-level program selective set two (Statistics major) in addition to those counted towards the major.
(c) For students with a major in Financial Mathematics and Statistics, 12 credit points of 2000-level program selective (Financial Mathematics and Statistics major) units in addition to those counted towards the major.
(d) For students with a major in Data Science, 6 credit points of 2000-level program selective (Data Science major) units in addition to those counted towards the major, and 6 credit points of 3000- or 4000-level selective units, in addition to those counted towards the major.

Units of study

The units of study are listed below.

1000-level units of study

Program Core (Non-Data Science major)
DATA1002 Informatics: Data and Computation

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project Work - own time Prohibitions: INFO1903 OR DATA1902 Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers computation and data handling, integrating sophisticated use of existing productivity software, e.g. spreadsheets, with the development of custom software using the general-purpose Python language. It will focus on skills directly applicable to data-driven decision-making. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model.
DATA1902 Informatics: Data and Computation (Advanced)

Credit points: 6 Session: Semester 2 Classes: lectures, laboratories Prohibitions: INFO1903 OR DATA1002 Assumed knowledge: This unit is intended for students with ATAR at least sufficient for entry to the BSc/BAdvStudies(Advanced) stream, or for those who gained Distinction results or better, in some unit in Data Science, Mathematics, or Computer Science. Students with portfolio of high-quality relevant prior work can also be admitted. Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit covers computation and data handling, integrating sophisticated use of existing productivity software, e. g. spreadsheets, with the development of custom software using the general-purpose Python language. It will focus on skills directly applicable to data-driven decision-making. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model. This unit includes the content of DATA1002, along with additional topics that are more sophisticated, suited for students with high academic achievement.
Program Core (Data Science major)
MATH1021 Calculus Of One Variable

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1,Semester 2,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1011 or MATH1901 or MATH1906 or ENVX1001 or MATH1001 or MATH1921 or MATH1931 Assumed knowledge: HSC Mathematics Extension 1 or equivalent. Assessment: 2 x quizzes (30%); 2 x assignments (5%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
Calculus is a discipline of mathematics that finds profound applications in science, engineering, and economics. This unit investigates differential calculus and integral calculus of one variable and the diverse applications of this theory. Emphasis is given both to the theoretical and foundational aspects of the subject, as well as developing the valuable skill of applying the mathematical theory to solve practical problems. Topics covered in this unit of study include complex numbers, functions of a single variable, limits and continuity, differentiation, optimisation, Taylor polynomials, Taylor's Theorem, Taylor series, Riemann sums, and Riemann integrals.
Textbooks
Calculus of One Variable (Course Notes for MATH1021)
MATH1921 Calculus Of One Variable (Advanced)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1001 or MATH1011 or MATH1906 or ENVX1001 or MATH1901 or MATH1021 or MATH1931 Assumed knowledge: HSC Mathematics Extension 2 or equivalent. Assessment: 2 x quizzes (20%); 2 x assignments (10%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Calculus is a discipline of mathematics that finds profound applications in science, engineering, and economics. This unit investigates differential calculus and integral calculus of one variable and the diverse applications of this theory. Emphasis is given both to the theoretical and foundational aspects of the subject, as well as developing the valuable skill of applying the mathematical theory to solve practical problems. Topics covered in this unit of study include complex numbers, functions of a single variable, limits and continuity, differentiation, optimisation, Taylor polynomials, Taylor's Theorem, Taylor series, Riemann sums, and Riemann integrals. Additional theoretical topics included in this advanced unit include the Intermediate Value Theorem, Rolle's Theorem, and the Mean Value Theorem.
Textbooks
As set out in the Junior Mathematics Handbook
MATH1931 Calculus Of One Variable (SSP)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1 Classes: 2x1-hr lectures; and 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1001 or MATH1011 or MATH1901 or ENVX1001 or MATH1906 or MATH1021 or MATH1921 Assumed knowledge: Band 4 in HSC Mathematics Extension 2 or equivalent. Assessment: Seminar participation (10%); 3 x special assignments (10%); 2 x quizzes (16%); 2 x assignments (8%); final exam (56%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Note: Enrolment is by invitation only
The Mathematics Special Studies Program is for students with exceptional mathematical aptitude, and requires outstanding performance in past mathematical studies. Students will cover the material of MATH1921 Calculus of One Variable (Adv), and attend a weekly seminar covering special topics on available elsewhere in the Mathematics and Statistics program.
MATH1023 Multivariable Calculus and Modelling

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1,Semester 2,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1013 or MATH1903 or MATH1907 or MATH1003 or MATH1923 or MATH1933 Assumed knowledge: MATH1X21, HSC Mathematics Extension 1 or equivalent. Assessment: 2 x quizzes (30%); 2 x assignments (5%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
Calculus is a discipline of mathematics that finds profound applications in science, engineering, and economics. This unit investigates multivariable differential calculus and modelling. Emphasis is given both to the theoretical and foundational aspects of the subject, as well as developing the valuable skill of applying the mathematical theory to solve practical problems. Topics covered in this unit of study include mathematical modelling, first order differential equations, second order differential equations, systems of linear equations, visualisation in 2 and 3 dimensions, partial derivatives, directional derivatives, the gradient vector, and optimisation for functions of more than one variable.
Textbooks
Multivariable Calculus and Modelling (Course Notes for MATH1023)
MATH1923 Multivariable Calculus and Modelling (Adv)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 2 Classes: 2x1-hr lectures; and 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1003 or MATH1013 or MATH1907 or MATH1903 or MATH1023 or MATH1933 Assumed knowledge: MATH1X21, HSC Mathematics Extension 2 or equivalent. Assessment: 2 x quizzes (20%); 2 x assignments (10%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Calculus is a discipline of mathematics that finds profound applications in science, engineering, and economics. This unit investigates multivariable differential calculus and modelling. Emphasis is given both to the theoretical and foundational aspects of the subject, as well as developing the valuable skill of applying the mathematical theory to solve practical problems. Topics covered in this unit of study include mathematical modelling, first order differential equations, second order differential equations, systems of linear equations, visualisation in 2 and 3 dimensions, partial derivatives, directional derivatives, the gradient vector, and optimisation for functions of more than one variable. Additional topics covered in this advanced unit of study include the use of diagonalisation of matrices to study systems of linear equation and optimisation problems, limits of functions of two or more variables, and the derivative of a function of two or more variables.
Textbooks
As set out in the Junior Mathematics Handbook
MATH1933 Multivariable Calculus and Modelling (SSP)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 2 Classes: 2x1-hr lectures; and 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1003 or MATH1903 or MATH1013 or MATH1907 or MATH1023 or MATH1923 Assumed knowledge: MATH1X21, Band 4 in HSC Mathematics Extension 2 or equivalent. Assessment: Seminar participation (10%); 3 x special assignments (10%); 2 x quizzes (16%); 2 x assignments (8%); final exam (56%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Note: Enrolment is by invitation only.
The Mathematics Special Studies Program is for students with exceptional mathematical aptitude, and requires outstanding performance in past mathematical studies. Students will cover the material of MATH1923 Multivariable Calculus and Modelling (Adv), and attend a weekly seminar covering special topics on available elsewhere in the Mathematics and Statistics program.

2000-level units of study

Program selective (Mathematics major)
DATA2902 Data Analytics: Learning from Data (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 2 Classes: Lecture 3 hrs/week + computer tutorial 2 hr/week Prerequisites: A mark of 65 or above in any of the following (DATA1001 or DATA1901 or ENVX1001 or ENVX1002) or (MATH10X5 and MATH1115) or (MATH10X5 and STAT2011) or (MATH1905 and MATH1XXX [except MATH1XX5]) or (QBUS1020 or ECMT1020 or STAT1021) Prohibitions: STAT2012 or STAT2912 or DATA2002 Assumed knowledge: Basic linear algebra and some coding for example MATH1014 or MATH1002 or MATH1902 and DATA1001 or DATA1901 Assessment: Computer practicals in-class (10%), Online quizzes (15%), Project group work assignment (5%), Project group work presentation (10%), Final exam (60%). Mode of delivery: Normal (lecture/lab/tutorial) day
Technological advances in science, business, and engineering have given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2902 is an intermediate unit in statistics and data sciences, focusing on learning advanced data analytic skills for a wide range of problems and data. How should the Australian government measure and report employment and unemployment? Can we tell the difference between decaffeinated and regular coffee? In this unit, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforcing their programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyse various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.
STAT2911 Probability and Statistical Models (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr computer lab/wk Prerequisites: (MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and a mark of 65 or greater in (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020) Prohibitions: STAT2011 Assessment: 2 x quizzes (10%); 2 x assignments (5%); computer work (5%); weekly computer lab reports (5%); a computer lab exam (10%) and a final 2-hr exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is essentially an advanced version of STAT2011, with an emphasis on the mathematical techniques used to manipulate random variables and probability models. Common distributions including the Poisson, normal, beta and gamma families as well as the bivariate normal are introduced. Moment generating functions and convolution methods are used to understand the behaviour of sums of random variables. The method of moments and maximum likelihood techniques for fitting statistical distributions to data will be explored. The notions of conditional expectation and prediction will be covered as will be distributions related to the normal: chi^2, t and F. The unit will have weekly computer classes where candidates will learn to use a statistical computing package to perform simulations and carry out computer intensive estimation techniques like the bootstrap method.
Textbooks
Mathematical Statistics and Data Analysis (3rd edition), J A Rice
Program selective set one (Statistics major)
MATH2921 Vector Calculus and Differential Eqs (Adv)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: [(MATH1921 or MATH1931 or MATH1901 or MATH1906) or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] and [(MATH1923 or MATH1933 or MATH1903 or MATH1907) or (a mark of 65 or above in MATH1023 or MATH1003)] Prohibitions: MATH2021 or MATH2065 or MATH2965 or (MATH2061 and MATH2022) or (MATH2061 and MATH2922) or (MATH2961 and MATH2022) or (MATH2961 and MATH2922) or MATH2067 Assessment: Quizzes (10%), assignments (20%); final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This is the advanced version of MATH2021, with more emphasis on the underlying concepts and mathematical rigour. The vector calculus component of the course will include: parametrised curves and surfaces, vector fields, div, grad and curl, gradient fields and potential functions, lagrange multipliers line integrals, arc length, work, path-independent integrals, and conservative fields, flux across a curve, double and triple integrals, change of variable formulas, polar, cylindrical and spherical coordinates, areas, volumes and mass, flux integrals, and Green's Gauss' and Stokes' theorems. The Differential Equations half of the course will focus on ordinary and partial differential equations (ODEs and PDEs) with applications with more complexity and depth. The main topics are: second order ODEs (including inhomogeneous equations), series solutions near a regular point, higher order ODEs and systems of first order equations, matrix equations and solutions, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, elementary Sturm-Liouville theory, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series). The unit then moves to topics in solution techniques for ordinary and partial differential equations (ODEs and PDEs) with applications. It provides a more thorough grounding in these techniques to enable students to build on the concepts in their subsequent courses. The main topics are: second order ODEs (including inhomogeneous equations), higher order ODEs and systems of first order equations, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series).
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2922 Linear and Abstract Algebra (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: MATH1902 or (a mark of 65 or above in MATH1002) Prohibitions: MATH2022 or MATH2968 or (MATH2061 and MATH2021) or (MATH2061 and MATH2921) or (MATH2961 and MATH2021) or (MATH2961 and MATH2921) Assessment: Online quizzes (10%); 2 x assignments (20%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Linear and abstract algebra is one of the cornerstones of mathematics and it is at the heart of many applications of mathematics and statistics in the sciences and engineering. This unit is an advanced version of MATH2022, with more emphasis on the underlying concepts and on mathematical rigour. This unit investigates and explores properties of vector spaces, matrices and linear transformations, developing general principles relating to the solution sets of homogeneous and inhomogeneous linear equations, including differential equations. Linear independence is introduced as a way of understanding and solving linear systems of arbitrary dimension. Linear operators on real spaces are investigated, paying particular attention to the geometrical significance of eigenvalues and eigenvectors, extending ideas from first year linear algebra. To better understand symmetry, matrix and permutation groups are introduced and used to motivate the study of abstract groups theory. The unit culminates in studying inner spaces, quadratic forms and normal forms of matrices together with their applications to problems both in mathematics and in the sciences and engineering.
Textbooks
As set out in the Intermediate Mathematics Handbook
Program selective set two (Statistics major)
MATH2921 Vector Calculus and Differential Eqs (Adv)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: [(MATH1921 or MATH1931 or MATH1901 or MATH1906) or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] and [(MATH1923 or MATH1933 or MATH1903 or MATH1907) or (a mark of 65 or above in MATH1023 or MATH1003)] Prohibitions: MATH2021 or MATH2065 or MATH2965 or (MATH2061 and MATH2022) or (MATH2061 and MATH2922) or (MATH2961 and MATH2022) or (MATH2961 and MATH2922) or MATH2067 Assessment: Quizzes (10%), assignments (20%); final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This is the advanced version of MATH2021, with more emphasis on the underlying concepts and mathematical rigour. The vector calculus component of the course will include: parametrised curves and surfaces, vector fields, div, grad and curl, gradient fields and potential functions, lagrange multipliers line integrals, arc length, work, path-independent integrals, and conservative fields, flux across a curve, double and triple integrals, change of variable formulas, polar, cylindrical and spherical coordinates, areas, volumes and mass, flux integrals, and Green's Gauss' and Stokes' theorems. The Differential Equations half of the course will focus on ordinary and partial differential equations (ODEs and PDEs) with applications with more complexity and depth. The main topics are: second order ODEs (including inhomogeneous equations), series solutions near a regular point, higher order ODEs and systems of first order equations, matrix equations and solutions, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, elementary Sturm-Liouville theory, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series). The unit then moves to topics in solution techniques for ordinary and partial differential equations (ODEs and PDEs) with applications. It provides a more thorough grounding in these techniques to enable students to build on the concepts in their subsequent courses. The main topics are: second order ODEs (including inhomogeneous equations), higher order ODEs and systems of first order equations, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series).
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2922 Linear and Abstract Algebra (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: MATH1902 or (a mark of 65 or above in MATH1002) Prohibitions: MATH2022 or MATH2968 or (MATH2061 and MATH2021) or (MATH2061 and MATH2921) or (MATH2961 and MATH2021) or (MATH2961 and MATH2921) Assessment: Online quizzes (10%); 2 x assignments (20%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Linear and abstract algebra is one of the cornerstones of mathematics and it is at the heart of many applications of mathematics and statistics in the sciences and engineering. This unit is an advanced version of MATH2022, with more emphasis on the underlying concepts and on mathematical rigour. This unit investigates and explores properties of vector spaces, matrices and linear transformations, developing general principles relating to the solution sets of homogeneous and inhomogeneous linear equations, including differential equations. Linear independence is introduced as a way of understanding and solving linear systems of arbitrary dimension. Linear operators on real spaces are investigated, paying particular attention to the geometrical significance of eigenvalues and eigenvectors, extending ideas from first year linear algebra. To better understand symmetry, matrix and permutation groups are introduced and used to motivate the study of abstract groups theory. The unit culminates in studying inner spaces, quadratic forms and normal forms of matrices together with their applications to problems both in mathematics and in the sciences and engineering.
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2923 Analysis (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 2 Classes: 3x1-hr lectures; 1x1-hr practice class and 1x1-hr tutorial/wk Prerequisites: [(MATH1921 or MATH1931 or MATH1901 or MATH1906) or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] and [(MATH1923 or MATH1933 or MATH1903 or MATH1907) or (a mark of 65 or above in MATH1023 or MATH1003)] Prohibitions: MATH2023 or MATH2962 or MATH3068 Assessment: 2 x quizzes (30%); an assignment (10%); and a final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Analysis grew out of calculus, which leads to the study of limits of functions, sequences and series. It is one of the fundamental topics underlying much of mathematics including differential equations, dynamical systems, differential geometry, topology and Fourier analysis. This advanced unit introduces the field of mathematical analysis both with a careful theoretical frame- work as well as selected applications.This unit will be useful to students with more mathematical maturity who study mathematics, science, or engineering. Starting off with an axiomatic description of the real numbers system, this unit concentrates on the limiting behaviour of sequences and series of real and complex numbers. This leads naturally to the study of functions defined as limits and to the notion of uniform con-vergence. Special attention is given to power series, leading into the theory of analytic functions and complex analysis. Besides a rigourous treatment of many concepts from calculus, you will learn the basic results of complex analysis such as the Cauchy integral theorem, Cauchy integral formula, the residues theorems, leading to useful techniques for evaluating real integrals. By doing this unit, you will develop solid foundations in the more formal aspects of analysis, including knowledge of abstract concepts, how to apply them and the ability to construct proofs in mathematics.
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2988 Number Theory and Cryptography Adv

Credit points: 6 Teacher/Coordinator: Prof Martin Wechslberger Session: Semester 2 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr computer lab/wk Prerequisites: [MATH19X1 or MATH1906 or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH19X3 or MATH1907 or (a mark of 65 or above in MATH1023 or MATH1003)] and [MATH1902 or (a mark of 65 or above in MATH1002)] Prohibitions: MATH2068 or MATH2088 Assessment: Quiz (10%); 2 x assignments (20%); final 2-hr exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is an advanced version of MATH2068, sharing the same lectures but with more advanced topics introduced in the tutorials and computer laboratory sessions.
Textbooks
Number Theory and Cryptography, R. Howlett, School of Mathematics and Statistics, University of Sydney, 2018.
MATH2970 Optimisation and Financial Mathematics Adv

Credit points: 6 Teacher/Coordinator: Prof Martin Wechslberger Session: Semester 2 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr computer lab/wk (lectures given in common with MATH2070). Prerequisites: [MATH19X1 or MATH1906 or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] Prohibitions: MATH2010 or MATH2033 or MATH2933 or MATH2070 or ECMT3510 Assumed knowledge: MATH19X3 or MATH1907 or a mark of 65 or above in MATH1003 or MATH1023 Assessment: 1 x2-hr exam (70%), 1 x assignment (10%), 1 x quiz (10%); 1 x computational project (10%). To pass the course at least 50% in the final exam is necessary. Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Students may enrol in both MATH2970 and MATH3975 in the same semester
The content of this unit of study parallels that of MATH2070, but students enrolled at Advanced level will undertake more advanced problem solving and assessment tasks, and some additional topics may be included.
Program selective (Financial Mathematics and Statistics major)
DATA2902 Data Analytics: Learning from Data (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 2 Classes: Lecture 3 hrs/week + computer tutorial 2 hr/week Prerequisites: A mark of 65 or above in any of the following (DATA1001 or DATA1901 or ENVX1001 or ENVX1002) or (MATH10X5 and MATH1115) or (MATH10X5 and STAT2011) or (MATH1905 and MATH1XXX [except MATH1XX5]) or (QBUS1020 or ECMT1020 or STAT1021) Prohibitions: STAT2012 or STAT2912 or DATA2002 Assumed knowledge: Basic linear algebra and some coding for example MATH1014 or MATH1002 or MATH1902 and DATA1001 or DATA1901 Assessment: Computer practicals in-class (10%), Online quizzes (15%), Project group work assignment (5%), Project group work presentation (10%), Final exam (60%). Mode of delivery: Normal (lecture/lab/tutorial) day
Technological advances in science, business, and engineering have given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2902 is an intermediate unit in statistics and data sciences, focusing on learning advanced data analytic skills for a wide range of problems and data. How should the Australian government measure and report employment and unemployment? Can we tell the difference between decaffeinated and regular coffee? In this unit, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforcing their programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyse various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.
MATH2921 Vector Calculus and Differential Eqs (Adv)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: [(MATH1921 or MATH1931 or MATH1901 or MATH1906) or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] and [(MATH1923 or MATH1933 or MATH1903 or MATH1907) or (a mark of 65 or above in MATH1023 or MATH1003)] Prohibitions: MATH2021 or MATH2065 or MATH2965 or (MATH2061 and MATH2022) or (MATH2061 and MATH2922) or (MATH2961 and MATH2022) or (MATH2961 and MATH2922) or MATH2067 Assessment: Quizzes (10%), assignments (20%); final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This is the advanced version of MATH2021, with more emphasis on the underlying concepts and mathematical rigour. The vector calculus component of the course will include: parametrised curves and surfaces, vector fields, div, grad and curl, gradient fields and potential functions, lagrange multipliers line integrals, arc length, work, path-independent integrals, and conservative fields, flux across a curve, double and triple integrals, change of variable formulas, polar, cylindrical and spherical coordinates, areas, volumes and mass, flux integrals, and Green's Gauss' and Stokes' theorems. The Differential Equations half of the course will focus on ordinary and partial differential equations (ODEs and PDEs) with applications with more complexity and depth. The main topics are: second order ODEs (including inhomogeneous equations), series solutions near a regular point, higher order ODEs and systems of first order equations, matrix equations and solutions, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, elementary Sturm-Liouville theory, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series). The unit then moves to topics in solution techniques for ordinary and partial differential equations (ODEs and PDEs) with applications. It provides a more thorough grounding in these techniques to enable students to build on the concepts in their subsequent courses. The main topics are: second order ODEs (including inhomogeneous equations), higher order ODEs and systems of first order equations, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series).
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2922 Linear and Abstract Algebra (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: MATH1902 or (a mark of 65 or above in MATH1002) Prohibitions: MATH2022 or MATH2968 or (MATH2061 and MATH2021) or (MATH2061 and MATH2921) or (MATH2961 and MATH2021) or (MATH2961 and MATH2921) Assessment: Online quizzes (10%); 2 x assignments (20%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Linear and abstract algebra is one of the cornerstones of mathematics and it is at the heart of many applications of mathematics and statistics in the sciences and engineering. This unit is an advanced version of MATH2022, with more emphasis on the underlying concepts and on mathematical rigour. This unit investigates and explores properties of vector spaces, matrices and linear transformations, developing general principles relating to the solution sets of homogeneous and inhomogeneous linear equations, including differential equations. Linear independence is introduced as a way of understanding and solving linear systems of arbitrary dimension. Linear operators on real spaces are investigated, paying particular attention to the geometrical significance of eigenvalues and eigenvectors, extending ideas from first year linear algebra. To better understand symmetry, matrix and permutation groups are introduced and used to motivate the study of abstract groups theory. The unit culminates in studying inner spaces, quadratic forms and normal forms of matrices together with their applications to problems both in mathematics and in the sciences and engineering.
Textbooks
As set out in the Intermediate Mathematics Handbook
Program selective (Data Science major)
MATH2921 Vector Calculus and Differential Eqs (Adv)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: [(MATH1921 or MATH1931 or MATH1901 or MATH1906) or (a mark of 65 or above in MATH1021 or MATH1001)] and [MATH1902 or (a mark of 65 or above in MATH1002)] and [(MATH1923 or MATH1933 or MATH1903 or MATH1907) or (a mark of 65 or above in MATH1023 or MATH1003)] Prohibitions: MATH2021 or MATH2065 or MATH2965 or (MATH2061 and MATH2022) or (MATH2061 and MATH2922) or (MATH2961 and MATH2022) or (MATH2961 and MATH2922) or MATH2067 Assessment: Quizzes (10%), assignments (20%); final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This is the advanced version of MATH2021, with more emphasis on the underlying concepts and mathematical rigour. The vector calculus component of the course will include: parametrised curves and surfaces, vector fields, div, grad and curl, gradient fields and potential functions, lagrange multipliers line integrals, arc length, work, path-independent integrals, and conservative fields, flux across a curve, double and triple integrals, change of variable formulas, polar, cylindrical and spherical coordinates, areas, volumes and mass, flux integrals, and Green's Gauss' and Stokes' theorems. The Differential Equations half of the course will focus on ordinary and partial differential equations (ODEs and PDEs) with applications with more complexity and depth. The main topics are: second order ODEs (including inhomogeneous equations), series solutions near a regular point, higher order ODEs and systems of first order equations, matrix equations and solutions, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, elementary Sturm-Liouville theory, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series). The unit then moves to topics in solution techniques for ordinary and partial differential equations (ODEs and PDEs) with applications. It provides a more thorough grounding in these techniques to enable students to build on the concepts in their subsequent courses. The main topics are: second order ODEs (including inhomogeneous equations), higher order ODEs and systems of first order equations, solution methods (variation of parameters, undetermined coefficients) the Laplace and Fourier Transform, an introduction to PDEs, and first methods of solutions (including separation of variables, and Fourier Series).
Textbooks
As set out in the Intermediate Mathematics Handbook
MATH2922 Linear and Abstract Algebra (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Daniel Hauer Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr practice class/wk Prerequisites: MATH1902 or (a mark of 65 or above in MATH1002) Prohibitions: MATH2022 or MATH2968 or (MATH2061 and MATH2021) or (MATH2061 and MATH2921) or (MATH2961 and MATH2021) or (MATH2961 and MATH2921) Assessment: Online quizzes (10%); 2 x assignments (20%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Linear and abstract algebra is one of the cornerstones of mathematics and it is at the heart of many applications of mathematics and statistics in the sciences and engineering. This unit is an advanced version of MATH2022, with more emphasis on the underlying concepts and on mathematical rigour. This unit investigates and explores properties of vector spaces, matrices and linear transformations, developing general principles relating to the solution sets of homogeneous and inhomogeneous linear equations, including differential equations. Linear independence is introduced as a way of understanding and solving linear systems of arbitrary dimension. Linear operators on real spaces are investigated, paying particular attention to the geometrical significance of eigenvalues and eigenvectors, extending ideas from first year linear algebra. To better understand symmetry, matrix and permutation groups are introduced and used to motivate the study of abstract groups theory. The unit culminates in studying inner spaces, quadratic forms and normal forms of matrices together with their applications to problems both in mathematics and in the sciences and engineering.
Textbooks
As set out in the Intermediate Mathematics Handbook
STAT2911 Probability and Statistical Models (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 1 Classes: 3x1-hr lectures; 1x1-hr tutorial; and 1x1-hr computer lab/wk Prerequisites: (MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and a mark of 65 or greater in (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020) Prohibitions: STAT2011 Assessment: 2 x quizzes (10%); 2 x assignments (5%); computer work (5%); weekly computer lab reports (5%); a computer lab exam (10%) and a final 2-hr exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is essentially an advanced version of STAT2011, with an emphasis on the mathematical techniques used to manipulate random variables and probability models. Common distributions including the Poisson, normal, beta and gamma families as well as the bivariate normal are introduced. Moment generating functions and convolution methods are used to understand the behaviour of sums of random variables. The method of moments and maximum likelihood techniques for fitting statistical distributions to data will be explored. The notions of conditional expectation and prediction will be covered as will be distributions related to the normal: chi^2, t and F. The unit will have weekly computer classes where candidates will learn to use a statistical computing package to perform simulations and carry out computer intensive estimation techniques like the bootstrap method.
Textbooks
Mathematical Statistics and Data Analysis (3rd edition), J A Rice

3000-level units of study

Selective
DATA3888 Data Science Capstone

Credit points: 6 Teacher/Coordinator: Prof Jean Yang Session: Semester 2 Prerequisites: DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012 Assessment: An disciplinary component 50% Online quiz = 10% Student lead lecture (10% report + 20% presentations + 10% peer review) An interdisciplinary component 50%Reflective task = 5%Team work process = 10%Report and product = 35% Mode of delivery: Normal (lecture/lab/tutorial) day
In our ever-changing world, we are facing a new data-driven era where the capability to efficiently combine and analyse large data collections is essential for informed decision making in business and government, and for scientific research. Data science is an emerging interdisciplinary field with its focus on high performance computation and quantitative expression of the confidence in conclusions, and the clear communication of those conclusions in different discipline context. This unit is our capstone project that presents the opportunity to create a public data product that can illustrate the concepts and skills you have learnt in this discipline. In this unit, you will have an opportunity to explore deeper disciplinary knowledge; while also meeting and collaborating through project-based learning. The capstone project in this unit will allow you to identify and place the data-driven problem into an analytical framework, solve the problem through computational means, interpret the results and communicate communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, to translate problem between two or more disciplines and this is essential for both professional and research pathways in the future.
FMAT3888 Projects in Financial Mathematics

Credit points: 6 Teacher/Coordinator: Prof Mary Myerscough Session: Semester 2 Classes: 2hr lectures and 3 hrs/workshops per week Prerequisites: (MATH2070 or MATH2970) and (STAT2011 or STAT2911) Assumed knowledge: STAT2X11, MATH2X70 Assessment: Discipline content assignment (10%), discipline content quiz (20%), Discipline project report (10%), discipline project presentation (10%), reflective task (10%), team work process (10%), interdisciplinary project report (20%), interdisciplinary oroject presentation (10%) Mode of delivery: Block mode
Mathematics and statistics are powerful tools in finance and more generally in the world at large. To really experience the power of mathematics and statistics at work, students need to identify and explore interdisciplinary links. Engagement with other disciplines also provides essential foundational skills for using mathematical and statistical ideas in financial contexts and in the world beyond. In this unit you will commence by working on a group project in an area of financial mathematics or statistics. From this project you will acquire skills of teamwork, research, wring and project management as well as disciplinary knowledge. You will then have the opportunity to apply your disciplinary knowledge in an interdisciplinary team to identify and solve problems and communicate your findings.
MATH3888 Projects in Mathematics

Credit points: 6 Teacher/Coordinator: Prof Mary Myerscough Session: Semester 2 Classes: 2hrs lectures and 3 hrs workshop per week Prerequisites: (MATH2921 or MATH2021 or MATH2065 or MATH2965 or MATH2061 or MATH2961 or MATH2923 or MATH2023) and (MATH2922 or MATH2022 or MATH2061 or MATH2961 or MATH2088 or MATH2988) Assessment: Discipline content assignment (10%), discipline content quiz (20%), Discipline project report (10%), discipline project presentation (10%), reflective task (10%), team work process (10%), interdisciplinary project report (20%), interdisciplinary oroject presentation (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
Mathematics is ubiquitous in the modern world. Mathematical ideas contribute to philosophy, art, music, economics, business, science, history, medicine and engineering. To really see the power and beauty of mathematics at work, students need to identify and explore interdisciplinary links. Engagement with other disciplines also provides essential foundational skills for using mathematics in the world beyond the lecture room. In this unit you will commence by working on a group project in an area of mathematics that interests you. From this you will acquire skills of teamwork, research, writing and project management as well as disciplinary knowledge. You will then have the opportunity to apply your disciplinary knowledge in an interdisciplinary team to indentify and solve problems and communicate your findings to a diverse audience.
MATH3975 Financial Derivatives (Advanced)

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 2 Classes: 3x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: Credit average or greater in 12 credit points of Intermediate Mathematics (including MATH2070 or MATH2970) Prohibitions: MATH3933 or MATH3015 or MATH3075 Assessment: 2 x assignments; 2-hr final exam (80%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will introduce you to the mathematical theory of modern finance with the special emphasis on the valuation and hedging of financial derivatives, such as: forward contracts and options of European and American style. You will learn about the concept of arbitrage and how to model risk-free and risky securities. Topics covered by this unit include: the notions of a martingale and a martingale measure, the fundamental theorems of asset pricing, complete and incomplete markets, the binomial options pricing model, discrete random walks and the Brownian motion, the Black-Scholes options pricing model and the valuation and heding of exotic options. Students completing this unit have been highly sought by the finance industry, which continues to need graduates with quantitative skills. Students enrolled in this unit at advanced level will have to undertake more challenging assessment tasks, but lectures in the advanced level are held concurrently with those of the corresponding mainstream unit.
STAT3021 Stochastic Processes

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 1 Classes: 3 lectures per week, tutorial 1hr per week. Prerequisites: STAT2X11 and (MATH1003 or MATH1903 or MATH1907 or MATH1023 or MATH1923 or MATH1933) Prohibitions: STAT3911 or STAT3011 Assessment: 2 x Quiz (2 x 15%), 2 x Assignment (2 x 5%), Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
A stochastic process is a mathematical model of time-dependent random phenomena and is employed in numerous fields of application, including economics, finance, insurance, physics, biology, chemistry and computer science. After setting up basic elements of stochastic processes, such as time, state, increments, stationarity and Markovian property, this unit develops important properties and limit theorems of discrete-time Markov chain and branching processes. You will then establish key results for the Poisson process and continuous-time Markov chains, such as the memoryless property, super positioning, thinning, Kolmogorov's equations and limiting probabilities. Various illustrative examples are provided throughout the unit to demonstrate how stochastic processes can be applied in modeling and analyzing problems of practical interest. By completing this unit, you will develop the essential basis for further studies, such as stochastic calculus, stochastic differential equations, stochastic control and financial mathematics.
STAT3922 Applied Linear Models (Advanced)

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 1 Classes: Three 1 hour lectures, one 1 hour tutorial and one 1 hour computer laboratory per week. Prerequisites: STAT2X11 and [a mark of 65 or greater in (STAT2X12 or DATA2X02)] Prohibitions: STAT3912 or STAT3012 or STAT3022 Assessment: 2 x assignment (10%), 3 x quizzes (35%), final exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using classical linear methods, together with concepts of collection of data and design of experiments. You will first consider linear models and regression methods with diagnostics for checking appropriateness of models, looking briefly at robust regression methods. Then you will consider the design and analysis of experiments considering notions of replication, randomization and ideas of factorial designs. Throughout the course you will use the R statistical package to give analyses and graphical displays. This unit is essentially an Advanced version of STAT3012, with additional emphasis on the mathematical techniques underlying applied linear models together with proofs of distribution theory based on vector space methods.
STAT3923 Statistical Inference (Advanced)

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 2 Classes: Three 1 hour lectures, one 1 hour tutorial and one 2 hour advanced workshop. Prerequisites: STAT2X11 and a mark of 65 or greater in (DATA2X02 or STAT2X12) Prohibitions: STAT3913 or STAT3013 or STAT3023 Assessment: 2 x Quizzes (20%), weekly homework (5%), Computer Lab Reports (10%), Computer Exam (10%), Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
In today's data-rich world more and more people from diverse fields are needing to perform statistical analyses and indeed more and more tools for doing so are becoming available; it is relatively easy to point and click and obtain some statistical analysis of your data. But how do you know if any particular analysis is indeed appropriate? Is there another procedure or workflow which would be more suitable? Is there such thing as a best possible approach in a given situation? All of these questions (and more) are addressed in this unit. You will study the foundational core of modern statistical inference, including classical and cutting-edge theory and methods of mathematical statistics with a particular focus on various notions of optimality. The first part of the unit covers various aspects of distribution theory which are necessary for the second part which deals with optimal procedures in estimation and testing. The framework of statistical decision theory is used to unify many of the concepts. You will rigorously prove key results and apply these to real-world problems in laboratory sessions. By completing this unit you will develop the necessary skills to confidently choose the best statistical analysis to use in many situations.
STAT3888 Statistical Machine Learning

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 2 Classes: Three 1 hour lectures, one 1 hour tutorial and one 1 hour computer laboratory per week. Prerequisites: STAT2X11 and (DATA2X02 or STAT2X12) Prohibitions: STAT3914 or STAT3014 Assumed knowledge: STAT3012 or STAT3912 or STAT3022 or STAT3922 Assessment: Written exam (40%), major project *50%), computer labs (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
Data Science is an emerging and inherently interdisciplinary field. A key set of skills in this area fall under the umbrella of Statistical Machine Learning methods. This unit presents the opportunity to bring together the concepts and skills you have learnt from a Statistics or Data Science major, and apply them to a joint project with NUTM3888 where Statistics and Data Science students will form teams with Nutrition students to solve a real world problem using Statistical Machine Learning methods. The unit will cover a wide breadth of cutting edge supervised and unsupervised learning methods will be covered including principal component analysis, multivariate tests, discrimination analysis, Gaussian graphical models, log-linear models, classification trees, k-nearest neighbors, k-means clustering, hierarchical clustering, and logistic regression. In this unit, you will continue to understand and explore disciplinary knowledge, while also meeting and collaborating through project-based learning; identifying and solving problems, analysing data and communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, and this is essential for both professional and research pathways in the future.

4000- level units of study

Selective
MATH4061 Metric Spaces

Credit points: 6 Teacher/Coordinator: Dr Florica Cirstea Session: Semester 1 Classes: Three 1 hour lectures and one 1 hour tutorial per week. Prerequisites: A mark of 65 or greater in 12cp from the following units (MATH3061 orMATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979) Prohibitions: MATH3961 Assumed knowledge: MATH2023 or MATH2923 or MATH2962 or MATH3068 Assessment: Quiz (10%), two assignments (2 x 10%) and a final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
At the end of this unit you will have received a broad introduction and gained a variety of tools to apply them within your further mathematical studies and/or in other disciplines.
MATH4062 Rings, Fields and Galois Theory

Credit points: 6 Teacher/Coordinator: Dr Florica Cirstea Session: Semester 1 Classes: 3 lectures 3 hrs/week; 1 tutorial 1 hr/week Prerequisites: (MATH2922 or MATH2961) or a mark of 65 or greater in (MATH2022 or MATH2061) or 12cp from (MATH3061 or MATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979) Prohibitions: MATH3062 or MATH3962 Assumed knowledge: MATH2922 or MATH2961 Assessment: 4 x homework assignments (4 x 5%), tutorial participation (10%), final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study lies at the heart of modern algebra. In the unit we investigate the mathematical theory that was originally developed for the purpose of studying polynomial equations. In a nutshell, the philosophy is that it should be possible to completely factorise any polynomial into a product of linear factors by working over a large enough field (such as the field of all complex numbers). Viewed like this, the problem of solving polynomial equations leads naturally to the problem of understanding extensions of fields. This in turn leads into the area of mathematics known as Galois theory. The basic theoretical tool needed for this program is the concept of a ring, which generalises the concept of a field. The course begins with examples of rings, and associated concepts such as subrings, ring homomorphisms, ideals and quotient rings. These tools are then applied to study quotient rings of polynomial rings. The final part of the course deals with the basics of Galois theory, which gives a way of understanding field extensions. Along the way you will see some beautiful gems of mathematics, including Fermat's Theorem on primes expressible as a sum of two squares, solutions to the ancient greek problems of trisecting the angle, squaring the circle, and doubling the cube, and the crown of the course: Galois' proof that there is no analogue of the quadratic formula for the general quintic equation. On completing this unit of study you will have obtained a deep understanding of modern abstract algebra.
MATH4063 Dynamical Systems and Applications

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 1 Classes: Three lectures, one tutorial per week Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from (MATH3061 or MATH3066 or MATH3076 or MATH3078 or MATH3961 or MATH3962 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)] Assumed knowledge: MATH2061 or MATH2961 or (MATH2X21 and MATH2X22) Assessment: Midterm exam (25%), two assignments (20% in total), final exam (55%). Mode of delivery: Normal (lecture/lab/tutorial) day
The theory of ordinary differential equations is a classical topic going back to Newton and Leibniz. It comprises a vast number of ideas and methods. The theory has many applications and stimulates new developments in almost all areas of mathematics. The emphasis is on qualitative analysis including phase-plane methods, bifurcation theory and the study of limit cycles. The more theoretical part includes existence and uniqueness theorems, linearisation, and analysis of asymptotic behaviour. The applications in this unit will be drawn from predator-prey systems, population models, chemical reactions, and other equations and systems from mathematical biology. You will learn how to use ordinary differential equations to model biological, chemical, physical and/or economic systems and how to use different methods from dynamical systems theory and the theory of nonlinear ordinary differential equations to find the qualitative outcome of the models. By doing this unit you will develop skills in using and analyzing nonlinear differential equations which will prepare you for further studies in mathematics, systems biology or physics or for careers in mathematical modelling.
MATH4068 Differential Geometry

Credit points: 6 Teacher/Coordinator: Dr Florica Cirstea Session: Semester 2 Classes: Three 1 hour lectures and one 1 hour tutorial per week. Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from (MATH3061 or MATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3961 or MATH3962 or MATH3963 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)] Prohibitions: MATH3968 Assumed knowledge: (MATH2921 and MATH2922) or MATH2961 Assessment: The grade is determined by student works throughout the semester, including Quiz 1 (10%), Assignment 1 (15%), Assignment 2 (15%), and Exam (60%). Moreover, to provide flexibility, the final grade is taken as the maximum between the above calculated score and the score of the exam out of 100. Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is an introduction to Differential Geometry, one of the core pillars of modern mathematics. Using ideas from calculus of several variables, we develop the mathematical theory of geometrical objects such as curves, surfaces and their higher-dimensional analogues. For students, this provides is the first taste of the investigation on the deep relation between geometry and topology of mathematical objects, highlighted in the classic Gauss-Bonnet Theorem. Differential geometry also plays an important part in both classical and modern theoretical physics. The course aims to develop geometrical ideas such as curvature in the context of curves and surfaces in space, leading to the famous Gauss-Bonnet formula relating the curvature and topology of a surface. A second aim is to remind the students about all the content covered in the mathematical units for previous years, most importantly the key ideas in vector calculus, along with some applications. It also helps to prepare the students for honours courses like Riemannian Geometry. By doing this unit you will further appreciate the beauty of mathematics which is originated from the need in solving practical problems / develop skills in understanding the geometry of the surrounding environment / prepare yourself for future study or the workplace by developing advanced critical thinking skills / gain a deep understanding of the underlying rules of the Universe.
MATH4069 Measure Theory and Fourier Analysis

Credit points: 6 Teacher/Coordinator: Dr Florica Cirstea Session: Semester 2 Classes: Three 1 hour lectures and one 1 hour tutorials per week. Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from the following units (MATH3061 or MATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3961 or MATH3962 or MATH3963 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)] Prohibitions: MATH3969 Assumed knowledge: (MATH2921 and MATH2922) or MATH2961 Assessment: 2 x quiz (20%), 2 x written assignment (20%), final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Measure theory is the study of fundamental ideas as length, area, volume, arc length and surface area. It is the basis for Lebesgue integration theory used in advanced mathematics ever since its development in about 1900. Measure theory is also a key foundation for modern probability theory. The course starts by establishing the basics of measure theory and the theory of Lebesgue integration, including important results such as Fubini's Theorem and the Dominated Convergence Theorem which allow us to manipulate integrals. These ideas are applied to Fourier Analysis which leads to results such as the Inversion Formula and Plancherel's Theorem. The Radon-Nikodyn Theorem provides a representation of measures in terms of a density. Key ideas of this theory are applied in detail to probability theory to provide a rigorous framework for probability which takes in and generalizes familiar ideas such as distributions and conditional expectation. When you complete this unit you will have acquired a new generalized way of thinking about key mathematical concepts such as length, area, integration and probability. This will give you a powerful set of intellectual tools and equip you for further study in mathematics and probability.
MATH4071 Convex Analysis and Optimal Control

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 1 Classes: Lecture 3hours/week, tutorial 1hr/week Prerequisites: [A mark of 65 or greater in 12cp from (MATH2070 or MATH2970 or STAT2011 or STAT2911 or MATH2021 or MATH2921 or MATH2022 or MATH2922 or MATH2023 or MATH2923 or MATH2061 or MATH2961 or MATH2065 or MATH2965 or MATH2962 or STAT2012 or STAT2912 or DATA2002 or DATA2902) or [12 cp from (MATH3075 or MATH3975 or STAT3021 or STAT3011 or STAT3911 or STAT3888 or STAT3014 or STAT3914 or MATH3063 or MATH3963 or MATH3061 or MATH3961 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)] Prohibitions: MATH3971 Assumed knowledge: MATH2X21 and MATH2X23 and STAT2X11 Assessment: Assignment (15%), assignment (15%), exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
The questions how to maximise your gain (or to minimise the cost) and how to determine the optimal strategy/policy are fundamental for an engineer, an economist, a doctor designing a cancer therapy, or a government planning some social policies. Many problems in mechanics, physics, neuroscience and biology can be formulated as optimisation problems. Therefore, optimisation theory is an indispensable tool for an applied mathematician. Optimisation theory has many diverse applications and requires a wide range of tools but there are only a few ideas underpinning all this diversity of methods and applications. This course will focus on two of them. We will learn how the concept of convexity and the concept of dynamic programming provide a unified approach to a large number of seemingly unrelated problems. By completing this unit you will learn how to formulate optimisation problems that arise in science, economics and engineering and to use the concepts of convexity and the dynamic programming principle to solve straightforward examples of such problems. You will also learn about important classes of optimisation problems arising in finance, economics, engineering and insurance.
MATH4074 Fluid Dynamics

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 1 Classes: Three 1 hour lectures and one 1 hour tutorial per week. Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from (MATH3061 orMATH3066 or MATH3063 or MATH3076 or MATH3078 or or MATH3961 or MATH3962 or MATH3963 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)] Prohibitions: MATH3974 Assumed knowledge: (MATH2961 and MATH2965) or (MATH2921 and MATH2922) Assessment: Assignment 1 (10%), Assignment 2 (10%), Assignment 3 (10%), Exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Fluid Mechanics is the study of systems which allow for a macroscopic description in some continuum limit. It is not limited to the study of liquids such as water but includes our atmosphere and even car traffic. Whether a system can be treated as a fluid, depends on the spatial scales involved. Fluid mechanics presents a cornerstone of applied mathematics and comprises a whole gamut of different mathematical techniques, depending on the question we ask of the system under consideration. The course will discuss applications from engineering, physics and mathematics. ò How and in what situations a system which is not necessarily liquid can be described as a fluid ò The link between an Eulerian description of a fluid and a Lagrangian description of a fluid ò The basic variables used to describe flows; the need for continuity, momentum and energy equations; simple forms of these equations; geometric and physical simplifying assumptions; streamlines and stream functions; incompressibility and irrotationality; simple examples of irrotational flows. By the end of this unit, students will have received a basic understanding into fluid mechanics and have acquired general methodology which they can apply in their further studies in mathematics and/or in their chosen discipline.
MATH4076 Computational Mathematics

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 1 Classes: Three 1 hour lectures and one 1 hour laboratory per week. Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from (MATH3061 or MATH3066 or MATH3063 or MATH3078 or or MATH3961 or MATH3962 or MATH3963 or MATH3969 or MATH3971 or MATH3974 or MATH3977 or MATH3978 or MATH3979)] Assumed knowledge: (MATH2X21 and MATH2X22) or (MATH2X61 and MATH2X65) Assessment: Quiz (15%), Assignment (15%), Assignment (15%), Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Sophisticated mathematics and numerical programming underlie many computer applications, including weather forecasting, computer security, video games, and computer aided design. This unit of study provides a strong foundational introduction to modern interactive programming, computational algorithms, and numerical analysis. Topics covered include: (I) basics ingredients of programming languages such as syntax, data structures, control structures, memory management and visualisation; (II) basic algorithmic concepts including binary and decimal representations, iteration, linear operations, sources of error, divide-and-concur, algorithmic complexity; and (III) basic numerical schemes for rootfinding, integration/differentiation, differential equations, fast Fourier transforms, Monte Carlo methods, data fitting, discrete and continuous optimisation. You will also learn about the philosophical underpinning of computational mathematics including the emergence of complex behaviour from simple rules, undecidability, modelling the physical world, and the joys of experimental mathematics. When you complete this unit you will have a clear and comprehensive understanding of the building blocks of modern computational methods and the ability to start combining them together in different ways. Mathematics and computing are like cooking. Fundamentally, all you have is sugar, fat, salt, heat, stirring, chopping. But becoming a good chef requires knowing just how to put things together in creative ways that work. In previous study, you should have learned to cook. Now you're going to learn how to make something someone else might want to pay for more than one time.
MATH4077 Lagrangian and Hamiltonian Dynamics

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 2 Classes: Three 1 hour lectures and one 1 hour tutorial per week. Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from (MATH3061 orMATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3961 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3978 or MATH3979)] Prohibitions: MATH3977 Assumed knowledge: 6cp of 1000 level calculus units and 3cp of 1000 level linear algebra and (MATH2X21 or MATH2X61) Assessment: One 2 hour exam (70%), two mid-term quizzes (10% each) and one assignment (10%). Mode of delivery: Normal (lecture/lab/tutorial) day
Lagrangian and Hamiltonian dynamics are a reformulation of classical Newtonian mechanics into a mathematically sophisticated framework that can be applied in many different coordinate systems. This formulation generalises elegantly to modern theories of relativity and quantum mechanics. The unit develops dynamics from the Principle of Least Action using the calculus of variations. Emphasis is placed on the relation between the symmetry and invariance properties of the Lagrangian and Hamiltonian functions and conservation laws. Coordinate and canonical transformations are introduced to make apparently complicated dynamical problems appear simpler. In this unit you will also explore connections between geometry and different physical theories beyond classical mechanics. You will be expected to solve fully dynamical systems of some complexity including planetary motion and to investigate stability using perturbation analysis. You will use Hamilton-Jacobi theory to solve problems ranging from geodesic motion (shortest path between two points) on curved surfaces to relativistic motion in the vicinity of black holes. This unit is a useful preparation for units in dynamical systems and chaos, and complements units in differential equations, quantum theory and general relativity.
MATH4078 PDEs and Applications

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 2 Classes: 3 lectures 1 hr/week; tutorial 1 hr/week Prerequisites: (A mark of 65 or greater in 12cp of 2000 level units) or [12cp from (MATH3061 or MATH3066 or MATH3063 or MATH3076 or MATH3961 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3979)] Prohibitions: MATH3078 or MATH3978 Assumed knowledge: (MATH2X61 and MATH2X65) or (MATH2X21 and MATH2X22) Assessment: Quiz (15%), Assignment (15%), Assignment (15%), Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Partial differential equations (PDEs) describe rates of change with respect to more than one variable, for example in both space and time or in two spatial directions. PDEs are powerful tools, used in a vast number of different applications, and of deep intrinsic mathematical interest. This unit will show you how to formulate and analyse PDEs in many different contexts. Many classical examples come naturally from physics, chemistry, and biology. But many more examples exist in areas, such as economics, finance, population dynamics, image analysis, and even the study of mathematics itself. Formulating and analysing PDEs was, for example, critical in proving the Poincare conjecture; a pure mathematical statement about the topology of spheres. This unit will teach you the tricks of the trade for modelling important systems with PDEs. You will learn both the technical skills needed to solve a challenging system of equations; and the insight needed to understand the meaning behind the mathematical expressions that result from solutions and formulations. When you complete this unit you will have a thorough foundational knowledge of classical PDE theory and application and be equipped for further study of research that uses PDE or for employment in scientific areas that use applications of PDEs.
MATH4079 Complex Analysis

Credit points: 6 Teacher/Coordinator: Prof Georg Gottwald Session: Semester 1 Classes: Lecture 3 hrs/week; tutorial 1 hr/week Prerequisites: (A mark of 65 or greater in 12cp of MATH2XXX units of study) or [12cp from the following units (MATH3061 or MATH3066 or MATH3063 or MATH3076 or MATH3078 or MATH3961 or MATH3962 or MATH3963 or MATH3968 or MATH3969 or MATH3971 or MATH3974 or MATH3976 or MATH3977 or MATH3978)] Prohibitions: MATH3979 or MATH3964 Assumed knowledge: MATH2X23 Assessment: 2 x assessment (30%), final exam worth (70%) (requires pass mark of 50% or more) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit will begin with a revision of properties of complex numbers and complex functions. This will be followed by material on conformal mappings, Riemann surfaces, complex integration, entire and analytic functions, the Riemann mapping theorem, analytic continuation, and Gamma and Zeta functions. Finally, special topics chosen by the lecturer will be presented, which may include elliptic functions, normal families, Julia sets, functions of several complex variables, or complex manifolds.
STAT4025 Time Series

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 1 Classes: 3 lectures, one tutorial and one computer class per week. Prerequisites: STAT2X11 and (MATH1X03 or MATH1907 or MATH1X23 or MATH1933) Prohibitions: STAT3925 Assessment: 2 x Quiz (20%), Computer lab participation / task completion (10%), Computer Exam (10%), Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will study basic concepts and methods of time series analysis applicable in many real world problems applicable in numerous fields, including economics, finance, insurance, physics, ecology, chemistry, computer science and engineering. This unit will investigate the basic methods of modelling and analyzing of time series data (ie. Data containing serially dependence structure). This can be achieved through learning standard time series procedures on identification of components, autocorrelations, partial autocorrelations and their sampling properties. After setting up these basics, students will learn the theory of stationary univariate time series models including ARMA, ARIMA and SARIMA and their properties. Then the identification, estimation, diagnostic model checking, decision making and forecasting methods based on these models will be developed with applications. The spectral theory of time series, estimation of spectra using periodogram and consistent estimation of spectra using lag-windows will be studied in detail. Further, the methods of analyzing long memory and time series and heteroscedastic time series models including ARCH, GARCH, ACD, SCD and SV models from financial econometrics and the analysis of vector ARIMA models will be developed with applications. By completing this unit, students will develop the essential basis for further studies, such as financial econometrics and financial time series. The skills gain through this unit of study will form a strong foundation to work in a financial industry or in a related research organization.
STAT4026 Statistical Consulting

Credit points: 6 Teacher/Coordinator: Dr John Ormerod Session: Semester 1 Classes: lecture 1 hr/week; workshop 2hrs/week Prerequisites: At least 12cp from STAT2X11, STAT2X12, DATA2X02 and STAT3XXX Prohibitions: STAT3926 Assessment: 4 x reports (40%), take-home exam report (40%), oral presentation (20%) Practical field work: Face to face client consultation: approximately 1 - 1.5 hrs/week Mode of delivery: Normal (lecture/lab/tutorial) day
In our ever-changing world, we are facing a new data-driven era where the capability to efficiently combine and analyse large data collections is essential for informed decision making in business and government, and for scientific research. Statistics and data analytics consulting provide an important framework for many individuals to seek assistant with statistics and data-driven problems. This unit of study will provide students with an opportunity to gain real-life experience in statistical consulting or work with collaborative (interdisciplinary) research. In this unit, you will have an opportunity to have practical experience in a consultation setting with real clients. You will also apply your statistical knowledge in a diverse collection of consulting projects while learning project and time management skills. In this unit you will need to identify and place the client's problem into an analytical framework, provide a solution within a given time frame and communicate your findings back to the client. All such skills are highly valued by employers. This unit will foster the expertise needed to work in a statistical consulting firm or data analytical team which will be essential for data-driven professional and research pathways in the future.