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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
Appendix A: Demo of feature engineering for text data
Section slides
Section 101
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
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
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
Attribution
LICENSE
.md
.pdf
Section 101
Section 101
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