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UBC CPSC 330: Applied Machine Learning (2024W1)

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 12: Ensembles
Lecture 13: Feature importances and model transparency
Lecture 14: Feature engineering and feature selection
Lecture 15: K-Means Clustering
Lecture 16: More Clustering
Lecture 17: Recommender Systems
Lecture 18: Introduction to natural language processing
Lecture 19: Multi-class classification and introduction to computer vision
Lecture 20: Time series
Lecture 21: Survival analysis
Lecture 22: Communication
Lecture 24: Deployment and conclusion
Final exam preparation: guiding questions
Appendix A: Demo of feature engineering for text data
Appendix B: Multi-class, meta-strategies

Section slides

Section 101
Section 102
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Lecture 17
- Lecture 18
- Lecture 19
- Lecture 20
- Lecture 21
- Lecture 22
- Lecture 23
- Lecture 24
Section 103
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Lecture 17
- Lecture 18
- Lecture 19
- Lecture 20
- Lecture 21
- Lecture 22
- Lecture 23
- Lecture 24

Attribution

LICENSE

.md

Lecture 23

Lecture 23#

Download the Lecture slides here as a PDF

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By Varada Kolhatkar

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