Exercises for 2021 MSc course in Machine Learning

Content of the course include:
• Supervised learning methods including K-nearest neighbours, decision trees, linear discriminant analysis, support vector machines, and neural networks.
• Unsupervised learning methods including K-means, Gaussian mixture model, hidden Markov model, EM algorithm, and principal component analysis.
• Probabilistic graphical models, variational Bayesian methods, belief propagation, and mean-field approximation.
• Bayesian decision theory, bias and variance trade-off, and cross-validation.
• Reinforcement learning.

Every Mini Module (MM) represents one lecture and there are 13 lectures in total. If there is an [x] sign, then it means that this lecture did not include any exercises. The topics are as following:

1. Introduction [x]
2. Bayesian decision theory [x]
3. Parametric and nonparametric methods
4. Dimensionality reduction
5. Clustering
6. Linear discrimination
7. Support vector machines
8. Multilayer perceptrons
9. Deep learning
10. Time series models [x]
11. Graphical models
12. Algorithm-independent machine learning
13. Reinforcement Learning [x]