Lecturer Varun Kanade
Hours Mondays and Wednesdays 17h-18h
Location L2 Mathematical Institute [map]

Note 1. Lecture notes will not be posted for every lecture, but only if the material covered deviates significantly from that found in the recommended textbooks. They should not be used as a substitute for actually reading the textbooks.

Note 2. Code used in the lecture slides is available here. You are encouraged to play with the code, both to improve your understanding of the material taught in the lectures as well as preparing you for the practicals.

Note 3. There will be parts of the chapters suggested as reading that you struggle with or may seem not directly related to what we covered in the lecture. I suggest you skip these sections and then only return to them once you've understood the material covered in the lecture and want to increase your understanding further.

Lecture Schedule

Date Topic Handouts Reading

10/10/2016 Introduction to Machine Learning [slides]
(Mur) Chap 1
(Bis) Chap 1

12/10/2016 Linear Regression [slides]

[wiki page]
17/10/2016 Maximum Likelihood [slides]
(Mur) Chap 2, Chap 7.1-4
(HTF) Chap 3.1-2

19/10/2016 Basis Expansion, Regularisation, Validation I [slides]
(Mur) Chap 7.5
(Bis) 3.1-3
(HTF) Chap 3.4, 3.6

24/10/2016 Basis Expansion, Regularisation, Validation II [slides]
(Mur) Chap 7.5-6
(Bis) 3.1-3
(HTF) Chap 3.4, 3.6, Chap 7

26/10/2016 Optimisation [slides]
(Mur) Chap 8.3, 8.5, Chap 13.1, 13.3-4
(Bis) App. C, E
(GBC) Chap. 8

31/10/2016 Classification: Generative Models [slides]
(Mur) Chap 3.5, 4.1-2
(Bis) Chap 4.1-2
(HTF) Chap 4.3

02/11/2016 Classification: Logistic Regression [slides]
(Mur) Chap 8.1-3, 8.6
(Bis) Chap 4.3
(HTF) Chap 4.4

07/11/2016 Support Vector Machines I [slides]
(Mur) Chap 14.5
(Bis) Chap 7.1
(HTF) Chap 12.1-2
(Optional) Reducing Multiclass to Binary

09/11/2016 Support Vector Machines II: Kernels [slides]
(Mur) Chap 14.1-2
(Bis) Chap 6.1-3
(HTF) Chap 12.3

14/11/2016 Feedforward Neural Nets & Backpropagation [slides]
(Nie) Chaps 1, 2
(GBC) Chap 6
(Optional) Understanding the difficulty of training deep feed forward neural networks
16/11/2016 Convolutional Neural Nets [slides]
(Nie) Chaps 5, 6
(GBC) Chaps 7, 9

21/11/2016 Dimensionality Reduction: PCA [slides]
(Mur) Chap 12.2
(Bis) Chap 12.1
(HTF) Chap 14.5

23/11/2016 Kernel PCA [slides]
(Mur) Chap 12.3, 14.4.4
(Bis) Chap 12.3
(HTF) Chap 14.5, 14.8

28/11/2016 Clustering, k-Means, Multidimensional Scaling, Hierarchical Clustering [slides]
(Mur) Chap, Chap 25.1, 25.3, 25.5
(Bis) Chap 9.1
(HTF) Chap 14.3, 14.8

30/11/2016 Spectral Clustering & Summary [slides]
(Mur) Chap 25.4