*"classification. If you are interested on Machine Learning, I would recommend you to get a copy of*— see post**Bishop's**book."*"into objects. More Info If you are interested in learning more, I strongly recommend that you read*— see post**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"*"in this type of machine learning are Pattern Classification , by Duda, Hart, and Stork, and*— see post**Pattern Recognition and Machine Learning**by Bishop. Some messy Python code that I wrote for implementing Poselets and Histogram of Oriented "*"am reading*— see post**Bishop's pattern recognition book**. In the decision theory part he first derives that using a quadratic loss function implies that our"*"Practical machine learning tools and techniques ("The Weka book") Christopher M. Bishop (2006)*— see post**Pattern Recognition and Machine Learning**Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (This one is a"

*"from scratch and had to self learn it. A good source for start understanding is the well known*— see post**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"*"Bishop (2006) Pattern Recognition and Machine Learning Russell, Stuart J.; Norvig, Peter (2003),*— see post**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 "*"can see you've rewritten your earlier question. The question you pose is explored in detail in*— see post**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"*"pathfinding tend to be closely related to graph and tree search algorithms. Significant Literature*— see post**AI: A Modern Approach**, Russell and Norvig Planning Algorithms , Lavalle Robot Motion Planning , Latombe External Links Pa"*"find the chapter on machine learning from*— see post**Russell & Norvig**is a pretty good place to start with SVMs. I think this is Chapter 18? One way to understand an SVM"

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

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

*"book on NLP. The subject is vast and really cool. A great book I've learned a lot from would be:*— see post**Speech and Language Processing**by Jurafski and Martin. And as a final thought, here's what I would do at a minimum: perform normal"*"book, you may need to read to get a lot of stuff related to NLP, and Question answering systems*— see post**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"*"and Martin's Speech and Language Processing*— see post**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"*"Foundations of Statistical Natural Language Processing is a nice introduction to statistical NLP.*— see post**Speech and Language Processing**is a bit more rigorous and maybe more authoritative. The Association for Computational Linguistics "*"books on Natural Language Processing Foundations of Statistical Natural Language Processing*— see post**Speech and Language Processing**NLTK Book"

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

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

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

*"up on machine learning applications, you can take a look at the following: Russel and Norvig's*— see post**Artificial Intelligence: A Modern Approach**, the standard text book for all things A.I. Journal of Machine Learning Research International Conf"*"Norvig's*— see post**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"*"standard textbook and a great place to start is Russel and Norvig's*— see post**Artificial Intelligence: A Modern Approach**. You can also get MIT's Intro AI course via OpenCourseWare"*"academic textbooks: Structure and Interpretation of Computer Programs Introduction to Algorithms*— see post**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"*"and the project. My guess is "no". I'd point you towards two sources. The first is Peter Norvig's*— see post**"Artificial Intelligence"**; the second is "Programming Collective Intelligence" . Maybe they'll inspire you."

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

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

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

*"changing the measurement units may even lead one to see a very different clustering structure:*— see post**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"*"degree in fuzzy clustering), which would subsequently allows us to spot noisy/ambiguous points.*— see post**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 "*"changing the measurement units may even lead one to see a very different clustering structure:*— see post**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"*"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.*— see post**Kaufman et al.**continues with some interesting considerations (page 11): From a philosophical point of view, stand"

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

*"in Computer Science and my thesis was about time-series prediction using Neural Networks. The book*— see post**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"*"the*— see post**Hands-On ML with Scikit-Learn**book, it states that, ...bagging ends up with a slightly higher bias than pasting, but... the ensem"*"Python Python for Data Analysis - book which nicely covers Pandas workflow with IPython.*— see post**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"*"So I've done a bit of research and found about the following books:*— see post**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"*"advice given verbatim from Aurélien Géron'*— see post**"Hands-On Machine Learning with Scikit-Learn and TensorFlow"**on DNN Architecture: - Initialization: He initialization - Activation function: ELU - Normalization"

*"to know what the community thinks about my possible choices, given my background and goals. (1)*— see post**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"*"basic statistics and the stats books above seem to be a bit much for you to kick off with, try*— see post**All of Statistics**, Mathematical Statistics and Data Analysis or Mathematical Statistics with Applications . Youtube h"*""Introduction to probability" by Blitzstein and Hwang (based on Harvard's statistics course);*— see post**"All of statistics: A concise course in statistical inference"**by Wasserman; Regression and Statistical Modeling Strategies "Regression modeling strategies: With "*"of the subjects ( textbooks only ). EDIT 1 : I think I may have found stats and probability book (*— see post**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"*"Methods by Westfall and Henning Probability for Statistics and Machine Learning by Das Gupta*— see post**All of Statistics**by Wasserman"

*"models in SPSS . For more thorough explanations of these topics, you may want to read Agresti's*— see post**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"*"try reading a book on logistic regression. Agresti's books are popular: Agresti, A. (2007).*— see post**An Introduction to Categorical Data Analysis**. Agresti, A. (2012). Categorical Data Analysis . Short of reading a whole book, you can start with"*"on its appropriateness to the situation, not based on fit. A good introduction to these issues is*— see post**Agresti's Introduction to Categorical Data Analysis**, although it doesn't cover beta regression. This paper provides a basic introduction to BR and how "*"personally like*— see post**Introduction to Categorical Data Analysis by Alan Agresti**and Introduction to Generalized Linear Models by Dobson and Barnett Both are very readable and spec"*"Agresti. He has put out a rigorous treatment of the subject ( Categorical Data Analysis ), and an*— see post**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"

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

*"books, I would recommend the works by Agresti. He has put out a rigorous treatment of the subject (*— see post**Categorical Data Analysis**), and an introductory version . For a quick guide to get you to the point where you can run a MLR, "*"regression (Wikipedia) is designed to do. It's covered in several textbooks; I learned out of*— see post**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"*"everything you need to know but lacks the derivation for the key results. Luckily Agresti's*— see post**Categorical Data Analysis**presents a good balance."*"popular: Agresti, A. (2007). An Introduction to Categorical Data Analysis . Agresti, A. (2012).*— see post**Categorical Data Analysis**. Short of reading a whole book, you can start with some of the threads on CV that discuss these to"*"am a big fan of Agresti's*— see post**Categorical Data Analysis**. I have read Agresti's Intro book but found it missing key interpretations for how generalized lin"

*"Other good ones are Hartley and Zissermann's Multi-View Projective Geometry and David Forsyth's*— see post**Computer Vision: A Modern Approach**"*"in general, I highly recommend taking a computer vision course and/or reading a textbook such as*— see post**Ponce and Forsyth's**."*"most popular ones are: http://www.amazon.com/Multiple-View-Geometry-Computer-Vision/dp/0521540518*— see post**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 "*"segmentation The two books that are pretty good on this subject are: Computer Vision: Shapiro*— see post**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 "*"to find matching points, there are volumes of research papers written on this topic and any*— see post**standard computer vision text**will have a chapter on this. Once you have N matching points, solving for the least squares transfo"

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

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

*"yCrCb_channels); unsigned char * pY = (uchar *) yCrCn_channels[1].data; Advice : read*— see post**http://www.amazon.com/dp/1849513244/?tag=stackoverfl08-20**, it covers the C++ opencv interface."*"are several approaches for detecting objects inside images. Just put some links here:*— see post**Open CV 2 Computer Vision Application Programming Cookbook**, Chapter 8/9 http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography."*"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*— see post**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"

*"used by machine learning/data mining people. If you are interested in basic theory, I recommend*— see post**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"*"stuff using this without really having to do any coding or math The makers have also published a*— see post**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"*"and the accompanying book Data Mining - Practical Machine Learning Tools and Techniques:*— see post**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"

*"on the complexity of your function. Genetic Algorithms could be a good candidate. Resources:*— see post**Genetic Algorithms in Search, Optimization, and Machine Learning**Implementing a Genetic Algorithms in C# Simple C# GA"*"can recommend*— see post**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"*"Low-level GA operators. But I found different texts says about different Low-level operators.*— see post**Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg**lists Dominance Inversrion Intra chromosomal duplication Deletion Translocation Segregation as low-"*"answer is very clear, but if you want more on the topic Goldberg's*— see post**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"

*"as $\mathbb{E}[\mathbf{Z}]$ using the values obtained from the EM algorithm? Additionally,*— see post**Marsland**'s Factor Analysis code implies that $$\mathbb{E}[\mathbf{Z}]=(WW^{\intercal}+\Psi)^{-1}W$$ so $$\ma"*"ML techniques, so two quite nice books (quite because unfortunately my favorite is in Polish):*— see post**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:/"*"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.*— see post**Machine Learning**, by Stephen Marsland is hands down the best practical book to pick up many of your requested topics"

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

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

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

*"should look for articles or books on " extractive summarization " A few starters could be: Books:*— see post**Natural Language Processing with Python**Foundations of Statistical Natural Language Processing Articles: Language independent extractive su"*"there is the builtin shlex lexical parsing library. There is also a recent book on the subject,*— see post**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"*"is the book I stumbled upon recently:*— see post**Natural Language Processing with Python**"