====== 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  

===== Materials =====

[[courses:be4m33ssu:micro-credentials:discriminative-learning|MCB1: Foundations of discriminative learning ]]

[[courses:be4m33ssu:micro-credentials:generative-learning|MCB2: Foundations of generative learning ]]