AI 221 - Classical ML - Code Repository

Code Repository for AI 221, a graduate course at the A.I. Program, College of Engineering, University of the Philippines, Diliman.

AI 221: Classical Machine Learning

Welcome to the GitHub repository for my course: AI 221 Classical Machine Learning. This repository contains Python codes (Jupyter notebooks), MATLAB codes, datasets, and lecture slides used in class.

AI 221 is intended for 2nd term graduate students at the Artificial Intelligence Program at the College of Engineering, University of the Philippines, Diliman. Pre-requisite knowledge on linear algebra (AI 211), optimization theory, basic statistics, and calculus are required.

Usage

The repository is organized into folders based on the weekly topics covered in the class, as follows:

• Week 1. Introduction to Machine Learning
• Week 2. Exploratory Data Analysis
• Week 3. Linear and Logistic Regression
• Week 4. Kernel Methods
• Week 5. Cross-validation and Hyper-parameter Search
• Week 6. Linear Dimensionality Reduction and LDA
• Week 7. Nonlinear Dimensionality Reduction
• Week 8. Clustering, Density Estimation, Anomaly Detection
• Week 9. Trees, Weak Learners, and Ensemble Learning
• Week 10. Neural Networks
• Week 11. Gaussian Processes and Bayesian Optimization
• Week 12. AutoML and Explainable AI (XAI)

Contributing

If you find any issues or have any suggestions for improvement, feel free to contact me via [e-mail]. If any codes are not working on your terminal, let me know. :)