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.
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. Gaussian Processes and Bayesian Optimization
- Week 6. Cross-validation and Hyper-parameter Search
- Week 7. Neural Networks
- Week 8. Trees, Weak Learners, and Ensemble Learning
- Week 9. Linear Dimensionality Reduction and LDA
- Week 10. Nonlinear Dimensionality Reduction
- Week 11. Clustering, Density Estimation, Anomaly Detection
- Week 12. AutoML and Explainable AI (XAI)
If you find any issues or have any suggestions for improvement, feel free to contact me via kspilario@up.edu.ph. If any codes are not working on your terminal, let me know. :)
