Overview

Title	Description
Lecture 1: Introduction to CPSC 330	What is machine learning, types of machine learning, learning to navigate through the course materials, getting familiar with the course policies
Lecture 2: Terminology, Baselines, Decision Trees	Supervised machine learning terminology: Features, target, examples, training, parameters and hyperparameters, Decision boundary, classification vs. regression, inference vs. prediction, accuracy vs. error, baselines, intuition of decision trees
Lecture 3: ML fundamentals	generalization, data splitting, overfitting, underfitting, the fundamental tradeoff, the golden rule
Lecture 4: \(k\)-nearest neighbours and SVM RBFs	introduction to KNNs, hyperparameter `n_neighbours` or \(k\), `C` and `gamma` hyperparameters of SVM RBF, decision boundaries with different values of hyperparameters.
Lecture 5: Preprocessing and sklearn pipelines	Pre-processing, Transformations, and pipelines.
Lecture 6: Column transformer and text features	Preprocessing and sklearn pipelines
Lecture 7: Linear models	Linear regression, logistic regression, prediction probabilities, sigmoid, interpretation of coefficients
CPSC 330 Lecture 8: Hyperparameter Optimization	Linear regression, logistic regression, prediction probabilities, sigmoid, interpretation of coefficients
CPSC 330 Lecture 9: Classification Metrics	confusion metrics, precision, recall, f1-score, PR curves, AP score, ROC curve, ROC AUC, class imbalance
CPSC 330 Lecture 10: Regression Metrics	Regression metrics
CPSC 330 Lecture 11: Ensembles
CPSC 330 Lecture 12: Feature importances
CPSC 330 Lecture 13: Feature Engineering and Selection
CPSC 330 Lecture 14: K-Means
CPSC 330 Lecture 15: DBSCAN, Hierarchical Clustering
CPSC 330 Lecture 16: Recommender Systems
CPSC 330 Lecture 17: Natural Language Processing
CPSC 330 Lecture 18: Introduction to deep learning and computer vision
CPSC 330 Lecture 19: Time series
CPSC 330 Lecture 20: Survival analysis
CPSC 330 Lecture 23: Deployment and conclusion
CPSC 330: Midterm 1 review