# Difference between revisions of "Resources"

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ToddNeller (Talk | contribs) (HMMs) |
ToddNeller (Talk | contribs) (→Hidden Markov Models) |
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** Russell and Norvig, Artificial Intelligence: a modern approach, 3rd ed, sections 15.3, 20.3.3. | ** Russell and Norvig, Artificial Intelligence: a modern approach, 3rd ed, sections 15.3, 20.3.3. | ||

** [https://web.stanford.edu/~jurafsky/slp3/9.pdf Speech and Language Processing, 3rd ed, Chapter 9 by Daniel Jurafsky and James H. Martin] | ** [https://web.stanford.edu/~jurafsky/slp3/9.pdf 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"] | + | ** [http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf 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 | ** Christopher Bishop's "Pattern Recognition and Machine Learning", Section 13.2 | ||

** [https://stats.stackexchange.com/questions/3294/resources-for-learning-markov-chain-and-hidden-markov-models StackExchange thread "Resources for learning Markov chain and hidden Markov models"] | ** [https://stats.stackexchange.com/questions/3294/resources-for-learning-markov-chain-and-hidden-markov-models StackExchange thread "Resources for learning Markov chain and hidden Markov models"] | ||

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** [http://cs.jhu.edu/~jason/papers/eisner.hmm.xls Jason Eisner's spreadsheet for teaching the forward-backward algorithm] and [https://cs.jhu.edu/~jason/papers/eisner.tnlp02.pdf his paper on using it for teaching] | ** [http://cs.jhu.edu/~jason/papers/eisner.hmm.xls Jason Eisner's spreadsheet for teaching the forward-backward algorithm] and [https://cs.jhu.edu/~jason/papers/eisner.tnlp02.pdf his paper on using it for teaching] | ||

** [https://www.quora.com/What-are-some-good-resources-for-learning-about-Hidden-Markov-Models Quora question "What are some good resources for learning about Hidden Markov Models?"] | ** [https://www.quora.com/What-are-some-good-resources-for-learning-about-Hidden-Markov-Models Quora question "What are some good resources for learning about Hidden Markov Models?"] | ||

− | * Model AI Assignments | + | * [http://modelai.gettysburg.edu Model AI Assignments] |

** [http://modelai.gettysburg.edu/2017/hmm Sravana Reddy's "Implementing a Hidden Markov Model Toolkit"] | ** [http://modelai.gettysburg.edu/2017/hmm Sravana Reddy's "Implementing a Hidden Markov Model Toolkit"] | ||

** [http://modelai.gettysburg.edu/2010/pacman/projects/tracking/busters.html John DeNero and Dan Klein's "The Pac-Man Projects" Project #4: Ghostbusters] | ** [http://modelai.gettysburg.edu/2010/pacman/projects/tracking/busters.html John DeNero and Dan Klein's "The Pac-Man Projects" Project #4: Ghostbusters] |

## Revision as of 23:47, 1 March 2018

## Contents

# Starting Places

# General

# Machine Learning

- Texts:
- 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

- Recommendations:

## Neural Network Learning

- Textbooks
- Websites
- 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
- Software
- Hardware

# 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