This repository includes my notes on MLAPP(Machine Learning: A Probabilistic Perspective, Kelvin P. Murphy).
The notes is composed of:
- Summary of the knowledge in the book.
- Some supplementary materials I collected to help understand the text.
- My understanding and ideas on the topics.
- My answers of the excecises.
Once I finished reading a chapter, I will upload a note in pdf.
Feel free to contact me if you have anything to supplement or question.
- Introduction
- Probability
- Generative models for discrete data
- Gaussian models
- Bayesian statistics
- Frequentist statistics
- Linear regression
- Logistic regression
- Generalized linear models and the exponential family
- Directed graphical models (Bayes nets)
- Mixture models and the EM algorithm
- Latent linear models
- Sparse linear models
- Kernels
- Gaussian processes
- Adaptive basis function models
- Markov and hidden Markov models
- State space models
- Undirected graphical models (Markov random fields)
- Exact inference for graphical models
- Variational inference
- More variational inference
- Monte Carlo inference
- Markov chain Monte Carlo (MCMC) inference
- Clustering
- Graphical model structure learning
- Latent variable models for discrete data
- Deep learning