#### About

Mathematics of machine learning. Overview of supervised, unsupervised and reinforcement learning. The course will cover neural networks, support vector machines, regression, clustering, PCA, collaborative filtering, as well important notions such as maximum likelihood, regularization and cross-validation.

#### Pre-requisites

Machine learning is a mathematical discipline; students will benefit from a solid background in probability, linear algebra and calculus. Programming experience and basic understanding of algorithms is essential. If you have doubts about your background, you should come talk to the lecturer as soon as possible.

#### Recommended Textbooks

- (HTF) T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning. Springer 2011. (Available for download on the authors' web-page.)
- (Mur) K. P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press 2012. (Electronic copy available through the Bodleian library.)
- (Bis) C. M. Bishop. Pattern Recognition and Machine Learning. Springer 2007.
- (Hay) S. Haykin. Neural Networks and Learning Machines. Pearson 2008.
- (Nie) M. Nielsen. Neural Networks and Deep Learning. Online Book available here.
- (GBC) I. Goodfellow, Y. Bengio, A. Courville. Deep Learning, MIT Press (in preparation). (Draft available here.)
- (SB) R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press (Online version and draft second edition here.)
- (Sze) C. Szepesvári. Algorithms for Reinforcement Learning. Morgan and Claypool. (Online version here.)

**Note:**Copies of the first four textbooks are available for short loan in the department library. Further copies may also be available in the RSL and college libraries.

#### Examination

The course will have a take-home final exam which you will pick on monday of week 8 of Hilary term and hand in on the monday of week 0 of Trinity term. Note that MFoCS students have an earlier submission date as set by the Maths Institute.

#### Contact Information

Apart from lectures, classes and practicals, we have set up a Piazza forum. Please sign up here. You should use this forum to ask questions which could be answered by your classmates, TAs, and the lecturer. Should you so wish, you can also post to this forum anonymously. You may also send private messages to the lecturer and teaching staff, though we recommend that you use this option sparingly.

If your question is of a personal nature, you should contact the lecturer directly via email. Please allow for at least 48 hours for an email response.

In any class, it can be challenging for the lecturer to gauge how smoothly the class is progressing. We always welcome any feedback on what we could be doing better. If you would like to send anonymous comments or criticisms, please feel free to use an anonymous emailer like this one to avoid revealing your identity.

#### Lectures

Lecturer | Varun Kanade |

Hours | 16:00-17:00 on Wednesday and Friday |

Location | Lecture Theatre B |

#### Lecture Schedule

Date | Topic | Handouts | Reading |
---|---|---|---|

20/01/2016 | Introduction to Machine Learning | [slides] |
HTF Chap. 1Mur Chap. 1Bis Chap. 1Hay Intro, Chap. 1 |

22/01/2016 | Linear Regression | [before] [after] |
HTF Chap. 2.1-3, Chap 3.1,3.2Mur Chap. 7.1-2, 7.4Bis Chap. 3.1 |

27/01/2016 | Maximum Likelihood | [before] [after] |
Normal distribution, Laplace distribution, Covariance, Entropy Mur Chap. 2.8, 4.1, 7.3-4Bis Chap. 2.4, 3.2 |

29/01/2016 | Basis Expansion, Regularization, Validation | [before] [after] |
Mur Chap. 6.4-5, 7.1-2HTF Chap. 3.4, 7Bis Chap. 1.3-5, 3.1-2 |

03/02/2016 | Regularization, Validation | [before] [after] |
Mur Chap. 6.4-5, 7.1-2HTF Chap. 3.4, 7Bis Chap. 1.3-5, 3.1-2 |

05/02/2016 | Optimisation | [before] [after] |
Linear Programming, Gradient, Hessian, Gradient Descent, SGD Tricks (Léon Bottou) |

10/02/2016 | Optimisation, Logistic Regression | [before] [after] |
Mur Chap 8 |

12/02/2016 | Multi-class Logistic Regression, SVMs | [before][after] |
Mur Chap 14.5HTF Chap 4.5, Chap 12 |

17/02/2016 | SVMs, Kernel Methods | [before][after] |
Mur Chap 14HTF Chap 4.5, Chap 12 |

19/02/2016 | Neural Networks, Backpropagation | [before][after] |
Nie Chap 1, 2GBC Chap 6 |

24/02/2016 | Training Neural Networks | [before][after] |
Nie Chap 3, 5GBC Chap 7,8 |

26/02/2016 | Convolutional Networks | [before][after] |
Nie Chap 6GBC Chap 9 |

02/03/2016 | Dimensionality Reduction and Multidimensional Scaling | [before][after] |
HSF Chap 14.5, 14.8Mur Chap 12.2-4 |

04/03/2016 | Clustering | [before][after] |
HSF Chap 14.3, 14.8Mur Chap 25.4-5 |

09/03/2016 | Clustering, Reinforcement Learning | [slides] |
SB Chap 1, 3 |

11/03/2016 | Reinforcement Learning | [slides] |
SB Chap 1, 3 |

#### Classes

Classes will be held during weeks 3-7. All classes except Groups 7 and 9 will be in Room 051 Wolfson Building; Group 7 will be in Room 013 in the Robert Hooke Building; Group 9 will be in Room 114 in the Robert Hooke Building. You**must**hand in the problem sheets by noon on the Friday before the class.

Group 1 | Monday | 10:00-11:00 | Abhishek Dasgupta |

Group 2 | Monday | 11:00-12:00 | Abhishek Dasgupta |

Group 3 | Wednesday | 11:00-12:00 | Rodrigo Mendoza Smith |

Group 4 | Wednesday | 12:00-13:00 | Rodrigo Mendoza Smith |

Group 5 | Friday | 10:00-11:00 | Francisco Marmolejo |

Group 6 | Friday | 11:00-12:00 | Francisco Marmolejo |

Group 7^{*} |
Monday | 14:00-15:00 | Syed (Ali) Rizvi |

Group 8 | Tuesday | 16:00-17:00 | Justin Bewsher |

Group 9^{†} |
Friday | 11:00-12:00 | Syed (Ali) Rizvi |

^{*}
This class will be in Room 013 in the Robert Hooke Building.

^{†}
This class will be in Room 114 in the Robert Hooke Building.

#### Problem Sheets

Please hand in your sheets before noon on Friday the week before the class.- Problem Sheet 1 (Week 3)
- Problem Sheet 2 (Week 4)
- Problem Sheet 3 (Week 5)
- Problem Sheet 4 (Week 6)
- Problem Sheet 5 (Week 7)
- Problem Sheet 6 (Week 8) (not for submission; no classes in week 8)

#### Practicals

Practicals will be held in weeks 2-8 in Room 379. The demonstrators for practicals are Abhishek Dasgupta, Bernardo Pérez Orozco, Marija Marcan.Group 1 | Tuesday | 10:00-12:00 |

Group 2 | Thursday | 09:00-11:00 |

Group 3 | Thursday | 11:00-13:00 |

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

**Installation:**The script provided in practical 1 will install torch on lab machines. For those using torch on their personal machines, we are unable to provide any support. The installation process is quite easy though and there is plenty of help available online.

All the practicals will be available on the Github repository.

Week 2 | Learning Lua and the tensor library | |

Week 3 | Linear Regression | |

Weeks 4, 5 | Optimization and MNIST Classification | |

Week 6 | MNIST Classification using SVM | |

Week 7 | Implementing a new layer | |

Week 8 | Experimenting with PCA |

#### Lecturer

Varun KanadeOffice Hours: Tuesday 15:30-16:30 in Room 449 Wolfson Building

#### Classes

Groups | Instructor |
---|---|

1, 2 | Abhishek Dasgupta |

3, 4 | Rodrigo Mendoza Smith |

5, 6 | Francisco Marmolejo |

7, 9 | Syed (Ali) Rizvi |

8 | Justin Bewsher |