Resources - Revision history

ToddNeller: CTR Prediction resources

2018-09-15T18:31:41Z

CTR Prediction resources

ToddNeller: /* Hidden Markov Models */

2018-03-02T03:47:54Z

‎Hidden Markov Models

ToddNeller: HMMs

2018-03-02T03:45:12Z

HMMs

ToddNeller: /* Neural Network Learning */

2018-01-05T15:55:49Z

‎Neural Network Learning

ToddNeller: /* Machine Learning */ adding kdnuggets link

2017-09-13T15:45:54Z

‎Machine Learning: adding kdnuggets link

ToddNeller: /* Neural Network Learning */ adding links

2017-08-30T14:39:42Z

‎Neural Network Learning: adding links

ToddNeller: /* Neural Network Learning */ adding links

2017-08-23T14:26:56Z

‎Neural Network Learning: adding links

ToddNeller: /* Neural Network Learning */ added Coursera specialization

2017-08-09T19:09:37Z

‎Neural Network Learning: added Coursera specialization

ToddNeller: /* Machine Learning */ addition

2017-07-24T20:10:17Z

‎Machine Learning: addition

← Older revision		Revision as of 18:31, 15 September 2018
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	** [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]
		+
		+	=Click-Through Rate (CTR) Prediction=
		+	* [https://www.kaggle.com/ Kaggle] CTR Prediction Competitions:
		+	** [https://www.kaggle.com/c/criteo-display-ad-challenge Criteo]
		+	** [https://www.kaggle.com/c/avazu-ctr-prediction Avazu]
		+	** [https://www.kaggle.com/c/avito-context-ad-clicks Avito]
		+	** [https://www.kaggle.com/c/outbrain-click-prediction Outbrain]
		+	* [https://xgboost.readthedocs.io/en/latest/ XGBoost] - Extreme Gradient Tree Boosting
		+	** [https://dl.acm.org/citation.cfm?doid=2939672.2939785 XGBoost: A Scalable Tree Boosting System]
		+	* [https://github.com/guestwalk/libffm libffm] - library for Field-aware Factorization Machines (FFMs)
		+	** [https://dl.acm.org/citation.cfm?doid=2959100.2959134 Field-aware factorization machines for CTR prediction]
		+	** [https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf Slides on winning approach to Criteo CTR Prediction competition]

@@ Line 77: / Line 77: @@
 ** 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]
-** [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
 ** [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"]
@@ Line 86: / Line 86: @@
 ** [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?"]
-* 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/2010/pacman/projects/tracking/busters.html John DeNero and Dan Klein's "The Pac-Man Projects" Project #4: Ghostbusters]

← Older revision		Revision as of 03:45, 2 March 2018
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	** [http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning Tim Dettmers' GPU comparison study]		** [http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning Tim Dettmers' GPU comparison study]
	** [https://d4datascience.wordpress.com/2017/05/06/setting-up-a-gpu-based-deep-learning-machine/ Ved's d4datascience blog entry on setting up a GPU-based deep learning machine]		** [https://d4datascience.wordpress.com/2017/05/06/setting-up-a-gpu-based-deep-learning-machine/ Ved's d4datascience blog entry on setting up a GPU-based deep learning machine]
		+
		+	=Hidden Markov Models=
		+	* Video Introductions
		+	** [https://youtu.be/mNSQ-prhgsw Daphne Koller's 12-minute video "Template Models: Hidden Markov Models - Stanford University"]
		+	** [https://youtu.be/jY2E6ExLxaw Nando de Freitas' 52-minute UBC lecture "undergraduate machine learning 9: Hidden Markov models - HMM"]
		+	* Texts and Articles
		+	** 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]
		+	** [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
		+	** [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"]
		+	* Other Resources
		+	** [https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 Udacity's `"Intro to Artificial Intelligence" course by Peter Norvig and Sebastian Thrun]
		+	** [https://www.udacity.com/course/probabalistic-models--cx27 Udacity's "Artificial Intelligence - Probabalistic Models"]
		+	** [https://www.coursera.org/learn/bioinformatics-pku/lecture/7pbUo/hidden-markov-model Coursera's Ge Gao lecture on HMMs]
		+	** [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?"]
		+	* Model AI Assignments
		+	** [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]

@@ Line 50: / Line 50: @@
 ** [https://medium.com/towards-data-science/secret-sauce-behind-the-beauty-of-deep-learning-beginners-guide-to-activation-functions-a8e23a57d046 Medium.com guide: Understanding Activation Functions]
 ** [https://github.com/williamFalcon/DeepRLHacks John Schulman's Deep RL Hacks (summarized by William Falcon)]
 *Online Courses
 ** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization]

@@ Line 22: / Line 22: @@
 *** [https://sites.ualberta.ca/~szepesva/RLBook.html Csaba Szepesvári. Algorithms for Reinforcement Learning]
 *** [http://dl.acm.org/citation.cfm?id=2670001 Marco Wiering and Martijn van Otterlo. Reinforcement Learning: State-of-the-Art]
 * Online resources:
 ** [https://www.coursera.org/learn/machine-learning Andrew Ng's free online Coursera Machine Learning course]

@@ Line 46: / Line 46: @@
 ** [http://playground.tensorflow.org/ TensorFlow Playground]
 ** [https://distill.pub/2017/momentum/ Why Momentum Really Works]
 *Online Courses
 ** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization]

@@ Line 44: / Line 44: @@
 ** [http://cs231n.github.io/convolutional-networks/ Stanford's CS 231n Convolutional Networks course website]
 ** various Wikipedia pages concerning [https://en.wikipedia.org/wiki/Artificial_neural_network artificial neural networks]
 *Online Courses
 ** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization]

@@ Line 29: / Line 29: @@
 ** [http://www.cs.waikato.ac.nz/ml/weka/ Weka Java-based Data Mining software] and [http://dl.acm.org/citation.cfm?id=1205860 Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques]
 ** [http://modelai.gettysburg.edu/ Model AI Assignments]
 * Recommendations:
 ** [https://www.quora.com/How-do-I-learn-machine-learning-1 Quora "How do I learn machine learning?" answers]