Probability
As is tradition, the prize pool has increased (to $300 this year).
I have collapsed first and second place into a winner-takes-all arrangement (c’est la vie).
Furthermore, there are additional changes to the structure of this Game:
- you must now pass the problem set to be awarded the prize money;
- you may submit your solutions to the problem set at any point in the future;
- if you plagiarise work, I reserve the right to ban you from all subsequent competitions — grim trigger
- the problem and solution set will now be courteously supported by MathJaX, TikZ, and my own JavaScript
- Good luck!
Links
Structure
Most notably, the structure from this year has changed. Instead of just offering a single PDF and then writing up solutions on this site, the problems themselves are accessible from below and once 2025 transpires, my solutions will be available as toggled nested environments.
Here we aim to understand how exactly “Logistic Regression”, a method that seems only to be used for classification problems, is indeed a regression algorithm.
We will as per the trend thus far, detail closed-form and approximate solutions to the loss function on this page. Furthermore, we will see how this type of regression is still a member of the GLM (generalised linear models) family, and we shall witness the derivation of the loss function by assuming our data is Bernoulli distributed.
| # | Course Code | Title | Offered | Prerequisites | Term | Type | Textbook | Notes |
|---|---|---|---|---|---|---|---|---|
| 1. | COMP6713 | Natural Language Processing | T1 | MATH1081,9444 | 26T1 | Elective | na | |
| 2. | FINS5513 | Investments and Portfolio Selection | T1,2,3 | 8750 program | 26T1 | Elective | na | |
| 3. | FINS5536 | Fixed Income Securities and Interest Rate Derivatives | T2 | 5513 | 26T2 | Elective | na | pricing, hedging, risk management. options, futures and swaps (int rate derivs) |
| 4. | MATH5856 | Introduction to Statistics and Statistical Computations | T2 | 26T2 | Elective | na | recommended for 5905 | |
| 6. | MATH5960 | Bayesian Inference and Computation | T3 | 2801/2901 | 26T3 | Elective | ||
| 7. | MATH5825 | Measure, Integration and Probability | T3 | U5705 | 26T3 | Elective | na | implicit prereq for 5835 |
| 8. | MATH5905 | Statistical Inference | T1,2,3 | U5846,U5856 | 27T1 | Core | na | |
| 9. | COMP9518 | Advanced Machine Learning | T2 | 9517 | 27T2 | Elective | na | |
| 10. | MATH5845 | Time Series | T2 | 27T2 | Elective | na | ||
| 11. | MATH5855 | Multivariate Analysis | T3 | 27T3 | Elective | na | ||
| 12. | MATH5835 | Advanced Stochastic Processes | T1 | U5825 | 28T1 | Core | na | Difficult. Requires an understanding of Real Analysis and Measure Theory |
| 13. | MATH5806 | Applied Regression Analysis | T2 | 28T2 | Elective | na | splines, poisson / binomial regression | |
| 14. | MATH5925 | Project (12uoc) | T1,2,3 | 36UoC | 28T2 | Core | na |
new plan ATTACH
| term (tentative) | course code | course name | UoC |
|---|---|---|---|
| 27T1 | MATH5975 | Introduction to Stochastic Analysis | 6 |
| 27T1 | MATH5371 | Numerical Linear Algebra | 6 |
| 27T2 | COMP9418 | Advanced Machine Learning | 6 |
| 27T3 | MATH5960 | Bayesian Inference and Computation | 6 |
| 28T1 | FINS5513 | Investments and Portfolio Selection | 6 |
| 28T1 | MATH5905 | Statistical Inference | 6 |
| 28T2 | FINS5536 | Fixed Income Securities & Interest Rate Derivatives | 6 |
| 28T2 | MATH5835 | Advanced Stochastic Processes | 6 |
| 29T1 | MATH5845 | Time Series | 6 |
| 29T1 | MATH5925 | Project | 6 |
| 29T2 | MATH5825 | Measure, Integration and Probability | 6 |
| 29T2 | MATH5925 | Project | 6 |
rebellion plan
handbook: https://www.handbook.unsw.edu.au/postgraduate/programs/2026/8750?year=2026
manufactured by amazon!
there is also a corresponding solution manual which I have found
credit for these solutions goes to: https://github.com/ilmoi/MML-Book
Backlinks (2)
1. Wiki /wiki/
Knowledge is a paradox. The more one understand, the more one realises the vastness of his ignorance.