Project competition

The project is typically a competition. The awards are extra marks out of the final 100%. The winner gets 15%, second place gets 10% and third place gets 5%. There are also 5 honorary 1% awards. The project is not recommended to everyone, but rather to those exceptional students who are considering a career in machine learning and data mining and would like more hands-on experience.


  • My favourite book for this course is the book of Stuart Russell and Peter Norvig titled artificial intelligence. Chapter 14 covers probabilistic graphical models. Chapter 15 covers HMMs. Chapter 20 talks about maximum likelihood, the EM algorithm, learning the parameters of graphical models and naive Bayes. Chapter 18 teaches decision trees, linear regression, regularization, neural networks and ensemble learning.
  • The machine learning book of Hastie, Tibshirani and Friedman is much more advanced, but it is also a great resource and it is free online: The elements of statistical learning.
  • For graphical models and Beta-Bernoulli models, I recommend A Tutorial on Learning with Bayesian Networks David Heckerman.
  • Kevin Murphy has compiled a nice page about Bayesian learning.
  • Wikipedia tutorial on the: SVD
  • The following handout should help you with linear algebra.