Syllabus

The following topics will be covered, subject to changes. See the course web page for a detailed schedule.

• Review of Probability
  • Conditional probability
  • Bayes’ Rule
• Introduction to Bayesian Data Analysis
  • Posterior inference
  • Conjugate Priors
  • MCMC sampling methods
• Multivariate Data Analysis
  • Review of linear algebra concepts
  • Singular value decomposition
  • Principal component analysis
  • Canonical Correlation Analysis
  • Multivariate regression
• Machine Learning Basics
  • Classification
  • Logistic Regression
  • Support Vector Machines
• Deep Learning Basics
  • Linear perceptron
  • Feedforward neural networks
  • Backpropagation
  • Stochastic gradient descent
  • Convolutional networks
  • Autoencoders
  • Generative models