Mathematics for Data Science 2 (2020)

Note that this is the OLD course (S2-2020). The current course is in UQ's Blackboard System

Welcome to MATH7502. This course is part of the Masters of Data Science Program at The University of Queensland. The course is coordinated and lectured by Yoni Nazarathy (y.nazarathy@uq.edu.au). The tutors are Samuel Hambleton (samuelahambleton@gmail.com) and Wala Draidi (wala.draidi@hotmail.com).

Outside of the Zoom lectures and practicals, communication dealing with course content matters should be via Piazza or via the Zoom consultation hours. Formal messages and grades are broadcasted via blackboard.

You may want to look at last year's course. A good part of the material is similar but the structure is different. You may also want to refer to the official course profile for grading information.

This course has a heavy focus on some of the mathematics used in data science applications. It is a linear algebra course that also incorporates some multi-variable calculus. The foundations of linear algebra are studied and explored with the help of numerical examples from the Julia programming language. The assignments and final project also celebrate these 12 data science use cases:
  1. Clustering
  2. Convergence proof for the perceptron
  3. Least squares data fitting
  4. Least squares classification
  5. Multi-objective least squares and regularization
  6. Multiple ways for evaluating least squares
  7. Linear dynamical systems and systems of differential equations
  8. Covariance matrices and joint probabilities
  9. Multi-variate Gaussian distributions and weighted least squares
  10. Cholesky decomposition for multi-variate random variable simulation
  11. Analysis of gradient descent and extensions
  12. Principal component analysis (PCA)
Background: The prerequisite for the course is knowledge comparable to MATH7501. This includes basic discrete mathematics, calculus and elementary manipulation of vectors and matrices. Feel free to use the 7501 - course reader to brush-up as needed. If you haven't done MATH7501, you may also use the mathematics first year learning centre to get help on elementary (MATH7501'ish) items such as basic matrix/vector operations, basic calculus, basic understanding of mathematical notation. It is also recommended that you read the following sections from the [VMLS] book as background: 1.1, 1.2, 1.3, 1.4, 3.1, 3.2, 6.1, 6.2, 6.3, 6.4, 7.1, 7.2, 7.3, 10.1, 10.2, 10.3.

The course makes use of the following resources and materials: References to [CLAWJ], [DSUC], [JULIA], [VMLS], [LALFD], [ILA], [3B1B], and [SWJ], are frequently made during the course and in each week students are requested to cover selected material from these sources.

The course assessment includes two assignments, three quizzes, and a final project: Quiz, assignment, and project submission information: All assessment items are to be submitted to mathdatasciencebridgingsubmissions@uq.edu.au (this account should not be used for any queries - only for submissions). Submissions should include two files, a PDF file and an audio clip. The submission must adhere to the following guidelines:

Lecture and practical recordings are here.

Other material from the lectures and practicals is in this GitHub repo.

Here is the schedule:

Week Monday Date Lecture Thursday Date Practical Assignment Due
1 Aug-3 2.5 hrs Aug-6 Julia intro -
2 Aug-10 2.5 hrs Aug-13 Julia linear algebra -
3 Aug-17 2.5 hrs Aug-20 Ass 1 guidance -
4 Aug-24 Quiz 1 + 1 hr Aug-27 Quiz 1 Sol -
5 Aug-31 2.5 hrs Sep-3 Ass 1 guidance -
6 Sep-7 2.5 hrs Sep-10 Quiz 2 prepare Ass 1 Due: Sep-10
7 Sep-14 Quiz 2 + 1 hr Sep-17 Quiz 2 Sol -
8 Sep-21 2.5 hrs Sep-24 Ass 2 guidance -
Break - - - - -
9 Public Holiday
- Oct-8 Ass 2 guidance -
10 Oct-12 2.5 hrs Oct-15 Quiz 3 prepare Ass 2 Due: Oct-15
11 Oct-19 Quiz 3 + 1 hr Oct-22 Quiz 3 sol -
12 Oct-26 2.5 hrs Oct-29 Project guidance -

Project due: Nov-14.