Machine Learning: 2014-2015
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. |
