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Syllabus

Course Logistics

Schedule and Deliverables
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Class Meetings

Class Meeting 1A
- Lecture 1
Class Meeting 1B
- Lecture 2
Class Meeting 1C
- Lecture 3
- Lecture 4
Class Meeting 2A
- Lecture 5
- Lecture 6
Class Meeting 2B
- Lecture 7
- Lecture 8
Class Meeting 3A
- Lecture 9
- Lecture 10
Class Meeting 3B - Review
- https://firasm.github.io/cpsc330-slides/slides-midterm1-review.html
Class Meeting 3C
- Lecture 11
Class Meeting 4A
- Lecture 12
Class Meeting 4B
- Lecture 13
Class Meeting 4C
- Lecture 14
- Lecture 15
Class Meeting 5A
- Lecture 16
- Lecture 17
Class Meeting 5B
Class Meeting 5C
- Lecture 18
- Lecture 19
Class Meeting 6A
- Lecture 20
- Lecture 21
Class Meeting 6B
- Lecture 22-Ethics Slides
- Lecture 23

Course Notes

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 exam preparation: guiding questions
Appendix A: Demo of feature engineering for text data
Appendix B: Multi-class, meta-strategies

Attribution

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Lecture 22 - Ethics

Lecture 22 - Ethics#

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Lecture 1: Course Introduction

By Varada Kolhatkar

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