CPSC 330 Section 4
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Lectures
Lecture 12: Ensembles
Overview
Lecture 1: Course introduction
Lecture 2: Terminology, baselines, decision trees
Lecture 3: ML fundamentals
Lecture 4: k-nearest neighbours and SVM RBFs
Lecture 5: Preprocessing and sklearn pipelines
Lecture 6:
sklearn
column transformer and text fearutres
Lecture 7: Linear models
Lecture 8: Hyperparameter Optimization
Lecture 9: Classification metrics
Lecture 10: Regression metrics
Lecture 11: Midterm review
Lecture 12: Ensembles
Lecture 13: Feature importances
Lecture 14: Feature engineering and selection
Lecture 15: K Means
Lecture 16: DBSCAN and hierarchical
Lecture 17: Recommender systems
Lecture 18: Natural Language Processing
Lecture 19: Introduction to computer vision
Lecture 20: Time series
Lecture 21: Survival analysis
Lecture 22: Ethics
Lecture 23: Communication and LLMs
Lecture 12: Ensembles
Random forests, gradient boosting, averaging, and stacking
Slides
Notebook
Outline
Motivation
Random forests
Gradient boosting
Averaging
Stacking
Lecture 11: Midterm review
Lecture 13: Feature importances