Course structure
Our course is structured as follows. A more detailed version can be found on the home page.
| Week | Topic | Lab | CW |
|---|---|---|---|
| 2 | Machine Learning: The Big Picture | LAB | - |
| 3 | K Nearest Neighbours and Decision Trees | LAB | CW1 |
| 4 | Evaluation of Machine Learning systems | LAB | CW1 |
| 5 | Neural Networks (Part 1) | LAB | CW1 |
| 6 | Neural Networks (Part 2) | LAB | CW2 |
| 7 | Unsupervised Learning: Clustering & Density Estimation | LAB | CW2 |
| 8 | Evolutionary Algorithms | LAB | CW2 |
The lab sessions will be held on Tuesdays 11am-1pm. These will be held on site in Huxley 219, 210, 221 and 225. The teaching assistants will most likely be lingering in the main lab (Huxley 219). We will try our best to station at least one teaching assistant in every lab. If you do not see a teaching assistant in your lab, please try visiting Huxley 219.
The live discussion/Q&A sessions will be held on Thursdays 10-11am. These will be held fully remotely on Microsoft Teams. If you are on campus, Huxley 311 is reserved for you to sit in, although no staff will be physically available in person. You will be manually added to a Microsoft Teams channel at some point in Week 2 if you are registered for the course at “Audit” or “Credit” level on the new module registration system on Scientia (or at least “Level 2” on the old system).