From AI Matters Wiki
- Christopher Bishop. Pattern Recognition and Machine Learning
- Kevin Murphy. Machine Learning: A Probabilistic Perspective
- David Barber. Probabilistic Reasoning and Machine Learning (Barber's free PDF version)
- Tom Mitchell. Machine Learning
- Ethem Alpaydin. Introduction to Machine Learning
- Statistical Learning:
- Reinforcement Learning:
- KDNuggets "10 Free Must-Read Books for Machine Learning and Data Science"
- Online resources:
- Andrew Ng's free online Coursera Machine Learning course
- UC Irvine Machine Learning Repository
- Kaggle datasets
- RStudio software for labs
- Weka Java-based Data Mining software and Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques
- Model AI Assignments
- Berkeley student crash course on ML
Neural Network Learning
- Waikit Lau and Arthur Chan's Artificial Intelligence and Deep Learning (AIDL) Facebook group and FAQ
- A Guide to Deep Learning by YerevaNN Labs
- Piotr Migdał's Learning Deep Learning with Keras
- a16z team's reference links
- Stanford's CS 231n Convolutional Networks course website
- various Wikipedia pages concerning artificial neural networks
- TensorFlow Playground
- Why Momentum Really Works
- Xiu-Shen Wei's Must Know Tips/Tricks in Deep Neural Networks
- Medium.com guide: Understanding Activation Functions
- John Schulman's Deep RL Hacks (summarized by William Falcon)
- Michael Dietz's Understand Deep Residual Networks
- Online Courses
Hidden Markov Models
- Video Introductions
- Texts and Articles
- Russell and Norvig, Artificial Intelligence: a modern approach, 3rd ed, sections 15.3, 20.3.3.
- Speech and Language Processing, 3rd ed, Chapter 9 by Daniel Jurafsky and James H. Martin
- Lawrence Rabiner tutorial "A tutorial on hidden Markov models and selected applications in speech recognition"
- Christopher Bishop's "Pattern Recognition and Machine Learning", Section 13.2
- StackExchange thread "Resources for learning Markov chain and hidden Markov models"
- Other Resources
- Udacity's `"Intro to Artificial Intelligence" course by Peter Norvig and Sebastian Thrun
- Udacity's "Artificial Intelligence - Probabalistic Models"
- Coursera's Ge Gao lecture on HMMs
- Jason Eisner's spreadsheet for teaching the forward-backward algorithm and his paper on using it for teaching
- Quora question "What are some good resources for learning about Hidden Markov Models?"
- Model AI Assignments