Department of Computer Science and Technology

Modelling and Machine Learning

This is a 15-hour summer course, offered in July 2023. It introduces machine learning from a probabilistic perspective, starting with elementary probability models and building up to advanced neural networks. It is a condensed version of material taught to second-year and fourth-year computer science students.

  • Lecture notes will be provided in the first lecture. The lecture notes cover more material than can fit into this course; the extra material is there for your curiosity.
  • Assessment. There are three assignments. Of these, one will be assessed in a written exam, and one will be assessed in a presentation. The other is not assessed, but you are encouraged to attempt it, and you are welcome to ask for help.
  • Slides will be posted here in the morning before each lecture. If you want to prepare, you should read the relevant section from lecture notes.
  • Videos that cover much of the same material (but with more examples) can be found on the course website for IB Data Science.
  • Prerequisites You should be familiar with basic probability and calculus. Make sure you can answer these exercises, then check your answers. The programming assignments assume you are familiar with Python and numpy. In case you aren't, here's a Python tutorial, a numpy tutorial, and a matplotlib tutorial.

Assignments

Schedule

Session 1 [slides]
(Tue 25 July, 09:00–12:00;
Seminar Room, CUED)
1, 1.1, 1.2. What is a probability model?
1.3, 1.4. Maximum likelihood estimation; numerical optimization
1.5. Maths notation for specifying models
1.6, 1.7. Generative and supervised modelling
fitting.ipynb
Session 2 [slides]
(Wed 26 July, 09:00–12:00;
Seminar Room, CUED)
3.1, 3.2. Classification with a neural network
3.3. Optimization using pytorch
3.4. Latent variable generative models
4. Assessing model fit
2. Regression: linear models and feature design
nn.ipynb, lm.ipynb
Mock exam question and solution walkthrough
Session 3 [slides]
(Thu 27 July, 09:00–12:00;
Teaching Room, CUED)
5.1. Bayes's rule
8.1. Bayesianism
6.1, 6.2, 8.2. Monte Carlo integration, computational Bayes's rule
8.3, 8.5. Bayesian readouts
6.3. Importance sampling
bayes.ipynb
Session 4 [slides]
(Mon 31 July, 09:00–12:00;
Seminar Room, CUED)
6.3, 6.4, 6.5. Variational autoencoders
10.1, 10.2. Overfitting and cross-validation
12.1, 12.2. Markov chains
nn.ipynb
Session 5 [slides]
(Tue 1 Aug, 09:00–12:00;
LAB 113, Lord Ashcroft Bldg)
13.1, 13.2. Learning sequence models
10.1, 10.2. Overfitting and generalizability
nn.ipynb
Exam
(Thu 10 Aug, 09:00–10:30;
CUED)
 An open-book written exam, testing
  • linear models (lecture 2) and confidence ribbons (lecture 3)
  • fitting a sequence model (lecture 5) and hypothesis testing (lecture 5)
Final presentation
(Wed 16 Aug, 09:00–12:00;
UCL Cruciform Bldg room B1.03)
Present your answers to the Particle Filter programming project

Instructor notes

Instructor notes — ask DJW for access