Lectures
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] [notes] |
(Mur) Chap 1 (Bis) Chap 1 |
12/10/2016 | Linear Regression |
[slides] [notes] |
[wiki page] |
17/10/2016 | Maximum Likelihood |
[slides] [notes] |
(Mur) Chap 2, Chap 7.1-4 (HTF) Chap 3.1-2 |
19/10/2016 | Basis Expansion, Regularisation, Validation I |
[slides] [notes] |
(Mur) Chap 7.5 (Bis) 3.1-3 (HTF) Chap 3.4, 3.6 |
24/10/2016 | Basis Expansion, Regularisation, Validation II |
[slides] [notes] |
(Mur) Chap 7.5-6 (Bis) 3.1-3 (HTF) Chap 3.4, 3.6, Chap 7 |
26/10/2016 | Optimisation |
[slides] [notes] |
(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] [notes] |
(Mur) Chap 3.5, 4.1-2 (Bis) Chap 4.1-2 (HTF) Chap 4.3 |
02/11/2016 | Classification: Logistic Regression |
[slides] [notes] |
(Mur) Chap 8.1-3, 8.6 (Bis) Chap 4.3 (HTF) Chap 4.4 |
07/11/2016 | Support Vector Machines I |
[slides] [notes] |
(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] [notes] |
(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 11.4.2.5, 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 |