List of topics:

• General Introduction
• Supervised Learning, Discriminative Algorithms:
  Supervised Learning Concept, Linear Regression, Maximum Likelihood, Normal Equation Gradient Descent, Stochastic Gradient, SVRG.
  Linear Classification, Logistic Regression, Newton Method,
• Generative Algorithms:
  Multivariate Normal, Linear Discriminant Analysis
  Naive Bayes, Laplacian Smoothing
  Multiclass Classification, K-NN
  Support Vector Machines and Kernel Methods:
  Intuition, Geometric Margins, Optimal Margin Classifier
  Lagrangian Duality, Soft-margin, Loss function, Stochastic Subgradient Method.
• Unsupervised Learning:
  PCA, Mixture Models, Bayesian Graphical Models
• Regularization and Model Selection:
• Decision Tree and Random Forest:
  Entropy, Bagging features, Bagging Samples, Random Forest
• Neural Network:
  Concept; Deep Neural Network; Backpropagation Convolutional Neural Network
• Causal Inference: 
  Potential outcomes, DiD, CiC, IVs.
• Applications to business analytics.

Supervised and unsupervised learning, Model selection, Generative models, causality, cases and applications to business analytics.

A course in basic probability theory and linear algebra. Ability to program in Python or Matlab.

Statistics.

Students should be familiar with basic concepts of probability theory, calculus and linear algebra.

• Formulate supervised and unsupsuervised learning problems and apply it to data.
• Analyze and apply generative models.
• Explore and train basic neural networks and apply them to data
• Identify causal inference vs association and perform inference tasks on data.

• Assess one's own level of skill acquisition, and plan their on-going learning goals.

Classical formal teaching interlaced with practical exercices.

Active participation in exercise sessions is essential.

30% Homework

30% Midterm project

40% Final project

• https://go.epfl.ch/MGT-424https://go.epfl.ch/MGT-424