Optimization Techniques for Machine Learning
UMAM-502
Course Resources
| File | Link | |
|---|---|---|
| 1 | Syllabus | Download |
| 2 | Textbook 1 | Download |
| 3 | Textbook 2 | Download |
| 4 | Prerequisite Diagnostic Test | Download |
Lecture Materials
| File | Link | |
|---|---|---|
| 1 | Lecture Notes - Mathematical Review | Download |
| 2 | Tutorial 1 | Download |
| 3 | Tutorial 1 - Solutions | Download |
| 4 | Detailed Proof - Fibonacci Search Method | Download |
| 5 | Simplex Method - Algorithm and Examples | Download |
Lab Materials
| File | Link | |
|---|---|---|
| 1 | Practical 1: Optimization Basics - A Computational Toolkit | Download |
| 2 | Practical 2: Newton's Method, Gradient Descent, Condition Number | Download |
| 3 | Optimization Lab | Visit |
Internal Exams
Extra Materials
| File | Link | |
|---|---|---|
| 1 | Extra 1: Gradient Descent Vs Newton's Method Vs Others | Download |
Key Text(s)
- Edwin K. P. Chong and Stanislaw H. Zak, An Introduction to Optimization, 4th Edition, Wiley (2013).
- Hamdy A. Taha, Operations Research – An Introduction, 10th Edition, Pearson (2017).
Suggested / Additional Readings
- SciPy Reference Guide — docs.scipy.org
- Christian Hill, Learning Scientific Programming with Python, 2nd Edition, CUP, 2020.
Course Snapshot
- L-T-P-C3-0-2-4
- Total Periods70
- Prerequisites10+2 Mathematics, Python programming, Calculus, Probability and Statistics
Prerequisite Resources
Limits, derivatives and applications.
Official Python getting-started guide.
Foundational probability & stats refresher.