BE4M33SSU - Micro credentials

We provide two micro-credential teaching blocks in the area of Statistical Machine Learning

• MCB1: Foundations of discriminative learning and empirical risk minimisation (2CT)
• MCB2: Foundations of generative learning and EM algorithm (2CT)

For each of them we provide lecture recordings, theoretical assignments as well as one homework assignment that requires you to code and experiment with a related applied task. Each block is completed by providing the solution for the homework and by undergoing a written online exam. We provide online consultations upon request.

• Prerequisites
  • Calculus and Linear Algebra: limits, derivatives, scalar product, matrix operations, eigenvectors
  • Optimisation: gradient descent, Lagrange formalism
  • Probability theory and statistics: random variables, conditional & marginal probabilities, standard distributions
  • Statistical machine learning: logistic regression, empirical risk, maximum likelihood estimate
  • Programming: Python and NumPy
• Grading/Credits
  • Weights for final grading: 40% practical labs + 60% written exam = 100% (+ bonus points)
  • Credits: 2 CP for each block

MCB1: Foundations of discriminative learning

MCB2: Foundations of generative learning