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Things you should know

Syllabus
CPSC 330 Documents
Course Learning Objectives

Lectures

Lecture 1: Course Introduction
Lecture 2: Terminology, Baselines, Decision Trees
Lecture 3: Machine Learning Fundamentals
Lecture 4: \(k\)-Nearest Neighbours and SVM RBFs
Lecture 5: Preprocessing and sklearn pipelines
Lecture 6: sklearn ColumnTransformer and Text Features
Lecture 7: Linear Models
Lecture 8: Hyperparameter Optimization and Optimization Bias
Lecture 9: Classification metrics
Lecture 10: Regression metrics
Lecture 11: Ensembles
Lecture 12: Feature importances and model transparency
Lecture 13: Feature engineering and feature selection
Lecture 14: K-Means Clustering
Lecture 15: More Clustering
Lecture 16: Recommender Systems
Lecture 17: Introduction to natural language processing
Lecture 18: Multi-class classification and introduction to computer vision
Lecture 19: Time series
Lecture 20: Survival analysis
Lecture 21: Communication
Lecture 23: Deployment and conclusion
Final review guiding questions
Bonus: A high-level quick introduction to LLMs

Demos

Lecture 3: Class demo
Lecture 4: Class demo
Lectures 5 and 6: Class demo
Lectures 7: Class demo
Exploring classification metrics
Lecture 14: Class demo
Lecture 15: Class demo

Appendices

Appendix A: Common features used in text classification
Appendix B: K-Means customer segmentation case study
Appendix C: Representing documents using embeddings

Attribution

Attributions
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By Varada Kolhatkar

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