Week 1:
 L01: T (Intro)
 L02: T (Decision trees)

Week 2:
 L03: T (Random forests)
 L04: T (Train/Test/validation)

Week 3:
 L05: K (Optimisation 1)
 L06: K (Optimisation 2)

Week 4:
 L07: K (Unsupervised)
 L08: K (Logistic)

Week 5:
 L09: T (Model Type)
 L10: T (Curse)

Week 6:
 Only labs week.

Week 7:
 L11: T (GM)
 L12: T (BP)

Week 8:
 L14: K (Optimisation 3)
 L13: T (Latent Variables)

Week 9:
 L15: K (Bayesian regression)
 L16: K (SVM)

Week 10:
 Guest: 3 short guest lectures about optional modules. K
 Recap:

Week 11: ?



Lab Week 1-2: 15% T
 Data exploration, plot some graphs, make a classifier by hand.

Lab Week 3-4: 15% K
 Decision tree.

Lab Week 5-6: 15% N
 Linear regression for optimisation.

Lab Week 7-8: 15% T
 Belief propagation.

Final project (Week 9-10 + Christmas):
 Single dataset.
 Both classification and regression.
 Impliment multiple algorithms.
 Go through full cycles of data/algorithm/test.
 Submit 3000 word report + Jupyter workbook(s).

