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Pattern Recognition and Machine Learning

Christopher M. Bishop

  • "classification. If you are interested on Machine Learning, I would recommend you to get a copy of Bishop's book."see post
  • "into objects. More Info If you are interested in learning more, I strongly recommend that you read Pattern Recognition and Machine Learning by Christopher M. Bishop or take a machine learning course. You may also be interested in reading, for free, the lecture not"see post
  • "in this type of machine learning are Pattern Classification , by Duda, Hart, and Stork, and Pattern Recognition and Machine Learning by Bishop. Some messy Python code that I wrote for implementing Poselets and Histogram of Oriented "see post
  • "am reading Bishop's pattern recognition book. In the decision theory part he first derives that using a quadratic loss function implies that our"see post
  • "Practical machine learning tools and techniques ("The Weka book") Christopher M. Bishop (2006) Pattern Recognition and Machine Learning Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (This one is a"see post

Artificial Intelligence: A Modern Approach (3rd Edition)

Stuart Russell and Peter Norvig

  • "from scratch and had to self learn it. A good source for start understanding is the well known Artificial Intelligence: A Modern Approach (3rd Edition) book. Note that I am not suggesting you to read the whole book at all, there is a chapter that talk"see post
  • "Bishop (2006) Pattern Recognition and Machine Learning Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (This one is a bit broader in focus, Peter Norvig is at Google, but was involved in the AI part of "see post
  • "can see you've rewritten your earlier question. The question you pose is explored in detail in Russell and Norvig's Artificial Intelligence: A Modern Approach. Read the 3rd chapter. Check out their website at http://aima.cs.berkeley.edu/ . They even have cod"see post
  • "pathfinding tend to be closely related to graph and tree search algorithms. Significant Literature AI: A Modern Approach, Russell and Norvig Planning Algorithms , Lavalle Robot Motion Planning , Latombe External Links Pa"see post
  • "find the chapter on machine learning from Russell & Norvig is a pretty good place to start with SVMs. I think this is Chapter 18? One way to understand an SVM"see post

Learning OpenCV: Computer Vision with the OpenCV Library

Gary Bradski and Adrian Kaehler

  • "a hands-on: Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski and Adrian Kaehler. This book will give you a nice introduction to a lot of CV topi"see post
  • "and installing OpenCV. 3. Now get and read some good books on OpenCV The best book on OpenCV is " Learning OpenCV " written by Gary Bradsky, main founder of OpenCV. Second one is " OpenCV cookbook ". These books c"see post
  • "powerful. A book that helped me a lot since there isn't a load of documentation on the web is Learning OpenCV. The documentation that comes with the API is good, but not great for learning how to use it. Relat"see post
  • "complex. I recommend you to read a chapter about background subtraction in official OpenCV book (http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134) as there are some of other similar techniques presented."see post
  • "would recommend a book by OpenCV author Gary Bradski - Learning OpenCV: Computer Vision with the OpenCV Library . It is not only a refence how to use OpenCV, but also a comprehensive book on many computer vision "see post

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

  • "suggest you to read this book (Although written for Python): Programming Collective Intelligence (http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325), they have a good example."see post
  • "you towards two sources. The first is Peter Norvig's "Artificial Intelligence" ; the second is "Programming Collective Intelligence". Maybe they'll inspire you."see post
  • "machine learning and data mining, you will want to look into collaborative filtering - I recommend this book. There is a lot of work in this field, notice how websites like Amazon have a feature that shows yo"see post
  • "again, this time looking more for .Net (idealy C#) ideas. A little background; I recently read Toby Segran's excellent book on CI, and I just got hold of Satnam Alag's book (which I am sure is also excellent, but I have only just"see post
  • "data mining fields. There is one book I would recommend, and I think it is tailored to your needs: Programming Collective Intelligence."see post

Speech and Language Processing, 2nd Edition

Daniel Jurafsky and James H. Martin

  • "book on NLP. The subject is vast and really cool. A great book I've learned a lot from would be: Speech and Language Processing by Jurafski and Martin. And as a final thought, here's what I would do at a minimum: perform normal"see post
  • "book, you may need to read to get a lot of stuff related to NLP, and Question answering systems http://www.amazon.com/Speech-Language-Processing-2nd-Edition/dp/0131873210 the book has a full section (V.Applications) that will help you a lot to develop a good system. but"see post
  • "and Martin's Speech and Language Processing http://www.amazon.com/Speech-Language-Processing-Daniel-Jurafsky/dp/0131873210/ is very good. Unfortunately the draft second edition chapters are no longer free online now that it"see post
  • "Foundations of Statistical Natural Language Processing is a nice introduction to statistical NLP. Speech and Language Processing is a bit more rigorous and maybe more authoritative. The Association for Computational Linguistics "see post
  • "books on Natural Language Processing Foundations of Statistical Natural Language Processing Speech and Language Processing NLTK Book"see post

Foundations of Statistical Natural Language Processing

Christopher D. Manning and Hinrich Schütze

  • "decent NLP textbook should have a chapter on language modelling. You might start with Chapter 6 of Manning and Schutze's "dice book" or Chapter 4 of Jurafsky and Martin . However, language models are so useful that they'll also show"see post
  • "extractive summarization " A few starters could be: Books: Natural Language Processing with Python Foundations of Statistical Natural Language Processing Articles: Language independent extractive summarization Extractive summarization: how to identify t"see post
  • "basis for all Natural Language Processing statistics related there is one must have book: Foundation of Statistical Natural Language Processing. Concretely to solve the problem of word/query similarity I have had good results with using Edit D"see post
  • "whole needed context, so will only approximate it. You can read more about n-grams in the classic Foundations of Statistical Natural Language Processing."see post
  • "About textbooks: Allen's Natural Language Understanding is a good, but a bit dated, book. Foundations of Statistical Natural Language Processing is a nice introduction to statistical NLP. Speech and Language Processing is a bit more rigorous an"see post

Pattern Classification (Pt.1)

Richard O. Duda, Peter E. Hart, and David G. Stork

  • "on large scale, but I don't remember details. I know where you can find them and I recommend this book for that."see post
  • "just predicted labels. Two good books to read to get started in this type of machine learning are Pattern Classification, by Duda, Hart, and Stork, and Pattern Recognition and Machine Learning by Bishop. Some messy Pytho"see post
  • "worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)"see post
  • "a couple: Introduction to Machine Learning by Nils J. Nilsson Machine Learning by Max Brammer Pattern Classification by Duda, Hart & Stork Let us know if you find others that are good."see post
  • "you want something around the pattern recognition and machine learning fields. If so, I like this book, which is a good introduction and talks a bit about applications to CV. It also assumes you have so"see post

Machine Learning: A Probabilistic Perspective

Kevin P. Murphy

  • "would go for a reference book on machine learning and graphical models. For example, Machine Learning: A Probabilistic Perspective Pattern Recognition and Machine Learning As for your question, latent variable models are graphical"see post
  • "read the following in Machine Learning: A Probabilistic Perspective: How can a uniform prior move the posterior mean? Isn't a uniform distribution supposed to not bias"see post
  • "LDA, shows nice pictures etc. but it would never even mention MANOVA (e.g. Bishop , Hastie and Murphy). Probably because people there are more interested in LDA classification accuracy (which roughly c"see post
  • "notion. In that sense, the quote from Section 11.3 is clearly confusing. In equation (10.27) of Murphy'sMachine Learning, the sampling model is such that it factorises as $$\prod_t p(\mathcal{D}_t|\theta_t)$$ And Murphy "see post
  • "am self-studying Kevin Murphy's book Machine learning - A probabilistic perspective and stumbled upon the following paragraph on biclustering. I understand the independence assumption"see post

Artificial Intelligence: A Modern Approach (2nd Edition)

Stuart Russell and Peter Norvig

  • "up on machine learning applications, you can take a look at the following: Russel and Norvig's Artificial Intelligence: A Modern Approach, the standard text book for all things A.I. Journal of Machine Learning Research International Conf"see post
  • "Norvig's Artificial Intelligence: A Modern Approach is a good book on general AI and explains a lot about the basics, and there is a section on Back Pr"see post
  • "standard textbook and a great place to start is Russel and Norvig's Artificial Intelligence: A Modern Approach . You can also get MIT's Intro AI course via OpenCourseWare"see post
  • "academic textbooks: Structure and Interpretation of Computer Programs Introduction to Algorithms Artificial Intelligence: A Modern Approach As well as a few textbooks I have left over from classes I've taken at a mediocre-at-best state uni"see post
  • "and the project. My guess is "no". I'd point you towards two sources. The first is Peter Norvig's "Artificial Intelligence"; the second is "Programming Collective Intelligence" . Maybe they'll inspire you."see post

Multiple View Geometry in Computer Vision

Richard Hartley and Andrew Zisserman

  • "to understand the maths behind it you may want to use a third party library as suggested above. Multiple View Geometry in Computer Vision by Hartkey and Zisserman and Three Dimensional Computer Vision: A Geometric Viewpoint by Faugeras"see post
  • "starting point) and then looking at a good computer vision book (e.g., Richard Szeliski's book or Hartley and Zisserman). But you are going to run into a host of practical problems. Consider that systems like Vuforia pr"see post
  • "You can also access the electronic drafts for free. Other good ones are Hartley and Zissermann's Multi-View Projective Geometry and David Forsyth's Computer Vision: A Modern Approach"see post
  • "chapter on Multiple View Geometry that contains most of the critical theory. In fact the textbook Multiple View Geometry in Computer Vision should also be quite useful (sample chapters available here ). Here's a page describing a project o"see post
  • "and a lot has been omitted. If you want to learn these concepts reading a good book like Multiple View Geometry in Computer Vision would be far better than reading some short articles. Often these short articles have several serio"see post

Neural Networks for Pattern Recognition

Christopher M. Bishop

  • "a comprehensive list of "best practices". One of the best books on the subject is Chris Bishop's Neural Networks for Pattern Recognition . It's fairly old by this stage but is still an excellent resource, and you can often find used copi"see post
  • "general architectures. A nice introductory paper is Neural networks and their applications . The book by the author is, IMHO, the best introductory text to the topic. Particularly, if you are willing t"see post
  • "with stochastic processes though, unless you really like math). Right next door is the subfield of neural networks. This is popular because you almost can't learn NN without building some interesting projects. Acro"see post
  • "areas of mathematics are important? I plan to read Neural Networks: A Systematic Introduction or Neural Networks for Pattern Recognition. Does anyone have any input or alternative recommendations?"see post
  • "like this one: http://neuron.eng.wayne.edu/bpFunctionApprox/bpFunctionApprox.html . Also, Bishop's book on NNs is the standard desk reference for anything to do with NNs."see post

Introduction to Machine Learning

Ethem Alpaydin

  • "Perspective by Stephen Marsland Pattern Recognition and Machine Learning by Christopher Bishop Introduction to Machine Learning - Ethem Alpaydin"see post
  • "can be found than greedy. A plethora of algorithms are applicable here, so I recommend chasing the classics and the literature for selecting the correct one that works within the constraints of your program."see post
  • "allowing the algorithm to react to new data. The trickiness of you point is briefly addressed in Introduction to Machine Learning By Ethem Alpaydin. On page 319 he derives the online k-means algorithm through the application of s"see post
  • "areas (less practical examples, and not business based, but more mathematically inclined) is: Introduction to Machine Learning, Ethem Alpaydin. edit: In response to your comment. If Python is your language of choice, look no f"see post
  • "(but is open source, so you may be able to glean some ideas from the source code. I also found Introduction to Machine Learning to provide a good overview, also reasonably priced, with a bit more math. Tools For creating visual"see post

Finding Groups in Data: An Introduction to Cluster Analysis

Leonard Kaufman and Peter J. Rousseeuw

  • "changing the measurement units may even lead one to see a very different clustering structure: Kaufman, Leonard, and Peter J. Rousseeuw.. "Finding groups in data: An introduction to cluster analysis." (2005). In some applications, changing the measurement units may even lead one to see a very different clus"see post
  • "degree in fuzzy clustering), which would subsequently allows us to spot noisy/ambiguous points. Kaufman, Leonard, and Peter J. Rousseeuw. "Finding groups in data: An introduction to cluster analysis." (2005), Chapter 4 explains this issue into more details. Excerpt: In a partition, each object of the data "see post
  • "changing the measurement units may even lead one to see a very different clustering structure: Kaufman, Leonard, and Peter J. Rousseeuw.. "Finding groups in data: An introduction to cluster analysis." (2005). In some applications, changing the measurement units may even lead one to see a very different clus"see post
  • "Groups in Data. An Introduction to Cluster Analysis from professors Leonard Kaufman and Peter J. Rousseeuw. I am reading the book and finding it very u"see post
  • "option of standardizing the data. This converts the original measurements to unitless variables. Kaufman et al. continues with some interesting considerations (page 11): From a philosophical point of view, stand"see post

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

Trevor Hastie, Robert Tibshirani, and Jerome Friedman

  • "Carnegie Mellon's Machine Learning Department . For another example, all three of the authors of Elements of Statistical Learning — which seems to be one of the standard textbooks in machine learning — are statisticians. On the o"see post
  • "level MIT , MIT2 , UCIrvine and machine learning courses as well. For Machine Learning I like Elements of Statistical Learning and Pattern Recognition and Machine Learning . Of course R has many more aspects than the ones cove"see post
  • "might find useful this one: The Elements of Statistical Learning: Data Mining, Inference, and Prediction UPDATE #1: This book might be useful as well: O'Reilly: Statistics in a Nutshel l"see post
  • "e.t.c. Introductory books on machine learning: [Flach] , [Mohri] , [Alpaydin] , [Bishop] , [Hastie] Books specific for SVMs: [Schlkopf] , [Cristianini] Some specific bibliography on document classifi"see post
  • "decisions. Reading This is very opinion-based, but at least few books are really worth mentioning: The Elements of Statistical Learning, An Introduction to Statistical Learning: with Applications in R or Pattern Recognition and Machine"see post

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

  • "in Computer Science and my thesis was about time-series prediction using Neural Networks. The book Hands on machine learning with Scikit and Tensorflow was extremely helpful from a practical point of view. It really lays things very clearly, without m"see post
  • "the Hands-On ML with Scikit-Learn book, it states that, ...bagging ends up with a slightly higher bias than pasting, but... the ensem"see post
  • "Python Python for Data Analysis - book which nicely covers Pandas workflow with IPython. Hands-On Machine Learning with Scikit-Learn and TensorFlow - slightly more advanced book about using Scikit-Learn and Tensor flow in data science projects R C"see post
  • "So I've done a bit of research and found about the following books: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Data Science from Scratch: First Principles with Python Python for Data Analysis: Data Wrangling wi"see post
  • "advice given verbatim from Aurélien Géron' "Hands-On Machine Learning with Scikit-Learn and TensorFlow" on DNN Architecture: - Initialization: He initialization - Activation function: ELU - Normalization"see post

All of Statistics: A Concise Course in Statistical Inference

Larry Wasserman

  • "to know what the community thinks about my possible choices, given my background and goals. (1) All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman. Looks nice to me but the author does not provide the answers to exercise proble"see post
  • "basic statistics and the stats books above seem to be a bit much for you to kick off with, try All of Statistics, Mathematical Statistics and Data Analysis or Mathematical Statistics with Applications . Youtube h"see post
  • ""Introduction to probability" by Blitzstein and Hwang (based on Harvard's statistics course); "All of statistics: A concise course in statistical inference" by Wasserman; Regression and Statistical Modeling Strategies "Regression modeling strategies: With "see post
  • "of the subjects ( textbooks only ). EDIT 1 : I think I may have found stats and probability book (All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman). Just need to figure out how to put the remaining pieces together. I welcome an"see post
  • "Methods by Westfall and Henning Probability for Statistics and Machine Learning by Das Gupta All of Statistics by Wasserman"see post

An Introduction to Categorical Data Analysis

Alan Agresti

  • "models in SPSS . For more thorough explanations of these topics, you may want to read Agresti's Introduction to Categorical Data Analysis. One thing I think you won't need to worry about is the size of the clusters though, these will wor"see post
  • "try reading a book on logistic regression. Agresti's books are popular: Agresti, A. (2007). An Introduction to Categorical Data Analysis . Agresti, A. (2012). Categorical Data Analysis . Short of reading a whole book, you can start with"see post
  • "on its appropriateness to the situation, not based on fit. A good introduction to these issues is Agresti's Introduction to Categorical Data Analysis , although it doesn't cover beta regression. This paper provides a basic introduction to BR and how "see post
  • "personally like Introduction to Categorical Data Analysis by Alan Agresti and Introduction to Generalized Linear Models by Dobson and Barnett Both are very readable and spec"see post
  • "Agresti. He has put out a rigorous treatment of the subject ( Categorical Data Analysis ), and an introductory version. For a quick guide to get you to the point where you can run a MLR, you may want to peruse the UCLA"see post

Reinforcement Learning: An Introduction

Richard S. Sutton and Andrew G. Barto

  • "every given state-action pair. If you are just learning all of this stuff I would recommend the Sutton and Barto text. Also if you want to see a simple example of a RL algorithm I have a very simple base class an"see post
  • "agents should be fairly commonplace among introductory RL stuff. A reference I recommend is Reinforcement Learning: An Introduction For more advanced/general references to learn policies for a variety of problems, I recommend the f"see post
  • "Sons, 2001. Tom Mitchell, Machine Learning . McGraw-Hill, 1997. Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998 For Natural Language Processing, the NLP group at Stanford provides many good reso"see post
  • "of reinforcement learning. From Reinforcement Learning: An Introduction Significant Literature Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, MIT Press, Cambridge, MA, 1998 External Links Reinforceme"see post
  • "slow (see codereview , ai to see the code and related questions). Figure taken from the book Reinforcement Learning An Introduction by Richard S. Sutton and Andrew G. Barto. My questions for this stack are the following: 1) How muc"see post

Categorical Data Analysis

Alan Agresti

  • "books, I would recommend the works by Agresti. He has put out a rigorous treatment of the subject ( Categorical Data Analysis ), and an introductory version . For a quick guide to get you to the point where you can run a MLR, "see post
  • "regression (Wikipedia) is designed to do. It's covered in several textbooks; I learned out of Categorical Data Analysis (Amazon link to 3e) by Agresti but it might not be the best book for self-study. You can also use a"see post
  • "everything you need to know but lacks the derivation for the key results. Luckily Agresti's Categorical Data Analysis presents a good balance."see post
  • "popular: Agresti, A. (2007). An Introduction to Categorical Data Analysis . Agresti, A. (2012). Categorical Data Analysis . Short of reading a whole book, you can start with some of the threads on CV that discuss these to"see post
  • "am a big fan of Agresti's Categorical Data Analysis . I have read Agresti's Intro book but found it missing key interpretations for how generalized lin"see post

Computer Vision: A Modern Approach

David A. Forsyth and Jean Ponce

  • "Other good ones are Hartley and Zissermann's Multi-View Projective Geometry and David Forsyth's Computer Vision: A Modern Approach"see post
  • "in general, I highly recommend taking a computer vision course and/or reading a textbook such as Ponce and Forsyth's."see post
  • "most popular ones are: http://www.amazon.com/Multiple-View-Geometry-Computer-Vision/dp/0521540518 http://www.amazon.com/Computer-Vision-Approach-David-Forsyth/dp/0130851981/ http://research.microsoft.com/en-us/um/people/szeliski/book/drafts/SzeliskiBook_20100423_draft.pdf "see post
  • "segmentation The two books that are pretty good on this subject are: Computer Vision: Shapiro Computer Vision A Modern Approach: Forsyth et al I used the CV: A modern approach for a CV class I took a semester or two ago. It is fairly concise "see post
  • "to find matching points, there are volumes of research papers written on this topic and any standard computer vision text will have a chapter on this. Once you have N matching points, solving for the least squares transfo"see post

Machine Learning

Tom M. Mitchell

  • "learning with Least Mean Square (LMS) rule (an exercise proposed in Tom Mitchell's famous book, Machine Learning). I made the computer learn by playing against an optimal opponent that picks the best moves, and t"see post
  • "find numerous descriptions of the m-estimate formula. A good reference text that describes this is Machine Learning by Tom Mitchell. The basic formula is P_i = (n_i + m*p_i) / (n + m) n_i is the number of training i"see post
  • "you are interested in Genetic Programming . A good base would be some reading (I would recommend this book on machine learning, it's great). Specifically for Genetic Programming you could try GPdotNET , but for broader Machine"see post
  • "Hart and David Stork, Pattern Classification , 2nd ed. John Wiley & Sons, 2001. Tom Mitchell, Machine Learning. McGraw-Hill, 1997. Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction . MIT "see post
  • "the sentences in new articles. My favourite reference on machine learning is Tom Mitchell's Machine Learning. It lists a number of ways to implement step (3). For question (4), I am sure there are a few paper"see post

Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis

Frank E. Harrell

  • "a variety of techniques, I recommend the package rms . You may also want to review Frank Harrell's book. You can validate, calibrate, plot, derive a formula, etc using the functions in the package."see post
  • "Further, he stated (and this is present on page 65 of his excellent Regression Modeling Strategies book) that collinearity between variables constructed in an algebraic fashion like restricted cubic spli"see post
  • "say what your DV is) One could use a spline of B instead (see, e.g. this article . If I recall, Frank Harrell's book discusses these models as well. However, a yes/no + number analysis might be easier to interpret."see post
  • "of problem, that you can work through. There are several books on logistic regression, including one by rms 's author."see post
  • "course in statistical inference" by Wasserman; Regression and Statistical Modeling Strategies "Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis" by Harrell; "Data analysis using regression and multilevel/hierarchical models" by Gelman and Hill;"see post

OpenCV 2 Computer Vision Application Programming Cookbook

Robert Laganière

  • "yCrCb_channels); unsigned char * pY = (uchar *) yCrCn_channels[1].data; Advice : read http://www.amazon.com/dp/1849513244/?tag=stackoverfl08-20 , it covers the C++ opencv interface."see post
  • "are several approaches for detecting objects inside images. Just put some links here: Open CV 2 Computer Vision Application Programming Cookbook, Chapter 8/9 http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography."see post
  • "2 Computer Vision Application Programming Cookbook was published in June 2011. It covers the newer C++ APIs, so it may be what you're looking for."see post
  • "opencv 2.2 for Microsoft visual studio 2010, I followed the instructions given in the book OpenCV 2 Computer Vision Application Programming Cookbook , but it’s not working, I performed the following steps: Compile opencv 2.2 using cmake, (source co"see post

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Ian H. Witten and Eibe Frank

  • "used by machine learning/data mining people. If you are interested in basic theory, I recommend Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten. For Java there is a great machine learning package, WEKA that is able to do assoc"see post
  • "stuff using this without really having to do any coding or math The makers have also published a pretty good textbook that explains the practical aspects of data mining Once you get the hang of it, you could use its A"see post
  • "Data Mining http://ecx.images-amazon.com/images/I/61DhYb1Z6QL._BO2,204,203,200_PIsitb-sticker-arrow-click,TopRight,35,-76_AA240_SH20_OU01_.jpg The last one is written by the same authors of Weka and contains a lot of examples but still, I fou"see post
  • "and the accompanying book Data Mining - Practical Machine Learning Tools and Techniques: http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0120884070 If you would like to read in more detail on what I just wrote above, it is explained in detail in C"see post

Genetic Algorithms in Search, Optimization, and Machine Learning

David E. Goldberg

  • "on the complexity of your function. Genetic Algorithms could be a good candidate. Resources: Genetic Algorithms in Search, Optimization, and Machine Learning Implementing a Genetic Algorithms in C# Simple C# GA"see post
  • "can recommend Genetic Algorithms in Search, Optimization, and Machine Learning by Goldberg. In particular, chapter 1 gives a great "introduction to genetic algorithms with examples." The cod"see post
  • "Low-level GA operators. But I found different texts says about different Low-level operators. Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg lists Dominance Inversrion Intra chromosomal duplication Deletion Translocation Segregation as low-"see post
  • "answer is very clear, but if you want more on the topic Goldberg's Genetic Algorithms in Search, Optimization, and Machine Learning has exhaustive coverage of schema theory and how to identify schematas for a given domain (with exa"see post

Machine Learning: An Algorithmic Perspective

Stephen Marsland

  • "as $\mathbb{E}[\mathbf{Z}]$ using the values obtained from the EM algorithm? Additionally, Marsland's Factor Analysis code implies that $$\mathbb{E}[\mathbf{Z}]=(WW^{\intercal}+\Psi)^{-1}W$$ so $$\ma"see post
  • "ML techniques, so two quite nice books (quite because unfortunately my favorite is in Polish): http://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1420067184 http://ai.stanford.edu/~nilsson/mlbook.html For numeric stuff like random number generation: http:/"see post
  • "Learning, Stephen Marsland. One of the best practical, Python based, texts I've come across."see post
  • "edit: In response to your comment. If Python is your language of choice, look no further. Machine Learning, by Stephen Marsland is hands down the best practical book to pick up many of your requested topics"see post

Econometric Analysis of Cross Section and Panel Data

Jeffrey M. Wooldridge

  • "such as a better control over endogeneity biases. Generally, I would recommend looking into the black Wooldridge to get it right."see post
  • "these other models may include J Scott Long's Regression Models for Limited Dependent Variables or Wooldridge's Econometric Analysis of Cross-Sectional and Panel Data. A lot of things in these books will appear odd to you as a computer scientist, though. But we must"see post
  • "There are many ways to do this, and this is just one. It is as presented in Wooldridge (2010), but you could find variations of this depending on your textbook. First off let os consider the fo"see post
  • "I know I ask for a lot.) To read more on simultaneous equations, see Chapter 9 of black Wooldridge and Chapter 4 of Bollen's Bible . Let me rewrite this as follows, with PCS =$y_1$, MCS =$y_2$, and "see post

The Art of R Programming: A Tour of Statistical Software Design

Norman Matloff

  • "books graph of The art of R programming has the following top 3 nodes: Indegree Centrality: The art of R programming - 18 outgoing edges R Cookbook (O'Reilly Cookbooks) - 14 outgoing edges Doing Bayesian Data Analysi"see post
  • ". But how would you interpret these results? Most important products? For example, books graph of The art of R programming has the following top 3 nodes: Indegree Centrality: The art of R programming - 18 outgoing edges R "see post
  • "I found the most insightful book for the pure programming side of R and as a reference to be The Art of R Programming. Great books to get you going on both the stats theory and R are: Linear Models With R , Extending "see post
  • "Bayesian Data Analysis: A Tutorial with R and BUGS - 10 outgoing edges Betweenness Centrality: The art of R programming - centrality value of 1210 What is a p-value anyway? - centrality value of 896 Visualize This - cen"see post

Fundamentals of Neural Networks: Architectures, Algorithms And Applications

Laurene V. Fausett

  • "found Fausett's Fundamentals of Neural Networks a straightforward and easy-to-get-into introductory textbook."see post
  • "found Fausett's Fundamentals of Neural Networks very accessible."see post
  • "calculate new weight matrix and so on... ... Aside: I'm implementing a Kohonen SOM algorithm fom this book."see post

Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit

Steven Bird, Ewan Klein, and Edward Loper

  • "should look for articles or books on " extractive summarization " A few starters could be: Books: Natural Language Processing with Python Foundations of Statistical Natural Language Processing Articles: Language independent extractive su"see post
  • "there is the builtin shlex lexical parsing library. There is also a recent book on the subject, Natural Language Processing with Python. It looks like at least part of it covers NLTK. You might also want to look at this list of tutoria"see post
  • "is the book I stumbled upon recently: Natural Language Processing with Python"see post