Statistical machine learning study guide

Description

Statistical machine learning

based on two books:

1. Probabilistic Machine Learning: An Introduction” by Kevin Patrick Murphy
2. Probabilistic Machine Learning: Advanced Topics

Topics includes:

• Probabilistic inference, Bayesian ML

• Univariate probabilisitic models

• Probabilistic graphical models, mixture models

• Regularization, First-order optimization, SGD, constrained optimization

• Bound optimization and EM, Black box optimization,Bayesian decision theory

• Naive Bayes, Logistic regression

• linear regression, ridge regression

• Bayes nets, Markov Random Fields

• Inference algorithms overview, Belief propagation in trees, Variable elimination

• Loopy BP, Junction tree algorithm, Rejection sampling

• Gibbs sampling, MCMC, Practical MCMC

The structure of the content are as follows:

• PDF <version>
  • Writing assignments and the programming assignment
  • Suggested solutions
• Latex+code <version>
  • Latex source of the solutions and the writing/programming assignments
  • Code for the programming assignment with the data in python
• Boudle
  • Includes all of the products and different versions.