Student Resources
PDF lecture notes and materials for all modules
Part A
Foundations of Statistical Learning
Module 01
What is Statistical Machine Learning?
▶ Open module
Module 02
Model Accuracy: Training vs Test Error
▶ Open module
Module 03
Bias-Variance Tradeoff in Depth
▶ Open module
Part B
Linear Models (Brief Review)
Module 04
Simple Linear Regression
▶ Open module
Module 05
Multiple Linear Regression
▶ Open module
Module 06
Model Assessment: R², F-test, CIs
▶ Open module
Module 07
Logistic Regression
▶ Open module
Part C
Classification Methods
Module 08
Linear Discriminant Analysis (LDA)
▶ Open module
Module 09
QDA and Naive Bayes
▶ Open module
Module 10
K-Nearest Neighbours
▶ Open module
Part D
Performance Measures
Module 11
Regression Metrics: MSE, RMSE, MAE, R²
▶ Open module
Module 12
Classification Metrics: Confusion Matrix, Precision, Recall, F1
▶ Open module
Module 13
ROC Curve, AUC, and Model Calibration
▶ Open module
Module 14
Cross-Validation
▶ Open module
Part E
Resampling and Model Selection
Module 15
The Bootstrap
▶ Open module
Module 16
Subset Selection: Best, Forward, Backward
▶ Open module
Module 17
Ridge Regression and Lasso
▶ Open module
Part F
Tree-Based Methods
Module 18
Introduction to Decision Trees: CART
Coming soon
Module 19
Classification Trees and Pruning
Coming soon
Part G
Dimensionality Reduction
Module 20
Principal Component Analysis (PCA)
Coming soon
Module 21
PCR and Partial Least Squares
Coming soon
Part H
Unsupervised Learning
Module 22
K-Means Clustering
Coming soon
Module 23
Hierarchical Clustering
Coming soon
Part I
Introduction to Artificial Intelligence
Module 24
Introduction to AI and Problem Formulation
Coming soon
Module 25
Uninformed Search: BFS and DFS
Coming soon
Module 26
Informed Search: Best-First Search and A*
Coming soon
Part J
Big Data
Module 27
Big Data and Distributed Computing
Coming soon
Module 28
Hadoop Ecosystem: HDFS, MapReduce, YARN, and Hive
Coming soon
Practice
Practice Problems and Revision
Practice 1
Practice Problems: Parts A to E
▶ Open module
Practice 2
Practice Problems: Parts F to J
Coming soon
Supplementary
Extra Topics
Extra 1
Ensemble Methods: Bagging, Random Forests, Boosting, and XGBoost
Coming soon
Extra 2
Nonlinear Models: Polynomial, Splines, and GAMs
Coming soon
Extra 3
Support Vector Machines: Theory and Kernels
Coming soon
Extra 4
Neural Networks and Deep Learning
Coming soon
Extra 5
Applied Case Study: A Complete Machine Learning Pipeline
Coming soon