Machine Learning Courses

Course materials

Lectures

This course is taught by Nando de Freitas.

Lecture 1: Introduction slides Video

Lecture 2: Linear prediction slides Video

Lecture 3: Maximum likelihood slides.pdf Video

Lectures 4 & 5: Regularizers, basis functions and cross-validation slides.pdf Video 1 Video 2

Lecture 6: Optimisation slides.pdf Video

Lecture 7: Logistic regression slides.pdf Video

Lecture 8: Back-propagation and layer-wise design of neural nets slides.pdf Video

Lecture 9: Neural networks and deep learning with Torch slides.pdf Video

Lecture 10: Convolutional neural networks slides.pdf Video

Lecture 11: Max-margin learning and siamese networks slides.pdf Video

Lecture 12: Recurrent neural networks and LSTMs slides.pdf Video

Lecture 13: Hand-writing with recurrent neural networks (Guest speaker: Alex Graves from Google Deepmind)

Lecture 14: Variational autoencoders and image generation (Guest speaker: Karol Gregor from Google Deepmind)

Lecture 15: Reinforcement learning with direct policy search slides.pdf Video

Lecture 16: Reinforcement learning with action-value functions slides.pdf Video

Practicals

Please click on Timetables on the right hand side of this page for time and location of the practicals. The instructors are Brendan Shillingford and Marcin Moczulsky.

Practicals will use Torch, a powerful programming framework for deep learning that is very popular at Google and Facebook research.

Practical on week 2: (1) Learning Lua and the tensor library. pdf

Practical on week 3: (2) Online and batch linear regression. pdf

Practical on week 4: (3) Logistic regression and optimization. pdf

Practical on week 5: continued previous practical.

Practical on week 6: (4) Feedforward neural networks, and implementing your own layer. pdf

Practical on week 7: (5) Intro to nngraph for graph-shaped modules. pdf

Practical on week 8: (6) Training a LSTM language model. pdf

See the Github repository list for the practicals’ code and technical instructions.

Classes

Please click on Timetables on the right hand side of this page for time and location of the classes. The exercises appear below and are due Thursdays at 1pm on the specified week.

Class on Week 3: Problem set. Due 1pm Thursday of Week 2.

Class on Week 5: Problem set. Due 1pm Thursday of Week 4.

Class on Week 7: Problem set. Due 1pm Thursday of Week 6.

Class on Week 8: Problem set. Due 1pm Thursday of Week 7.