Lecture 1: Introduction and Nearest Neighbours    (ESL 1, 2.1-2.3, 2.5.)

Lecture 2: Linear Regression    (ESL 2.9, 3.2.  PRML 1.0-1.1, 1.2.5, 3.1)  Contour maps

Lecture 3: Linear Classification: Logistic Regression    (ESL 4.1, 4.2, 4.4)

Lecture 4: Multi-class Classification and  Neural Nets   
    (Notes,    ESL 11.3, 11.4,  PRML 5.0, 5.1, 5.2.0, 5.2.4, 5.3.0, 5.3.1, 5.3.2)

Lecture 5: Probabilistic Models    (ESL 6.6.3,  PRML 1.2.0, 1.2.3, 2.1, 4.2.3)

Lecture 6: Probabilistic Models (continued)    (ESL 4.3.0,  PRML 1.2.4, 4.2)

Lecture 7: Dimensionality Reduction: PCA and Autoencoders

Lecture 8: Clustering: K-means and EM

Lecture 9: Reinforcement Learning

Lecture 10: Ensemble Methods  (PRML 14.2-14.3)

Lecture 11: Convolutional Neural Networks

Lecture 12: Decision Trees

Lecture 15: SVM  (Bishop: chapter 7,  pages 325-337)

Lecture 16: Kernels

Lecture 18: Ensemble Methods II

Lecture 19: Reinforcement Learning