The online book was created by The Data Mining group was established in November 2000 by Dr. Saed Sayad in a collaboration with Professor Stephen T. Balke in the Department of Chemical Engineering and Applied Chemistry at the University of Toronto.

You can visit the group here:

http://chem-eng.utoronto.ca/~datamining/

You can access the book here:

http://chem-eng.utoronto.ca/~datamining/dmc/data_mining_map.htm

Features:

• Quick Reference
• Easy to memorize
• Neat layout with good colored illustrations
• Easy navigation
• and more.

Happy Reading!

Download:

Associative Rule Mining
Graphical Models 1
Graphical Models 2
Graphical Models 3
Gaussian Processes
ML Introduction
Privacy Preserving Mining
Support Vector Machines

Schedule:

More details visit :http://bangalore.yahoo.com/labs/summerschool.html

On-Demand for Academics is an online program for teaching and learning data management and analytics. It allows professors and students to use several applications, including SAS Enterprise Guide and SAS Enterprise Miner for free. More applications may be added in the near future.  Is SAS Institue following in the foot steps of software giants?, its something which we should wait and watch!

[Bizjournals]

This was the first lecture:- (course materials) The first 30 minutes or so of this lecture is introduction to the course and the field of machine learning in general.

In the second lecture, Professor Andrew Ng will cover topics like linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

For more  visit the home page: http://cs229.stanford.edu/

The first video (YouTube) gives us a good idea about the initial stages of mining data like to import training and prediction data, to add a classification learner and then to apply the model.

The second video shows you how to use a cross and simple validation operator to split your training data into two sets: training and validation data sets.

The third video shows you how to create a decision tree to help us find “sweet spots” in a particular market segment. This video tutorial uses the Rapidminer direct mail marketing data generator and a split validation operator to build the decision tree.

After going through these videos, we are sure you will now be able to ease your way into RapidMiner.

Author: Randall Scott

Release Date: Jan 22, 2010

Website Release: Jan 22, 2010

Web page link: http://outskirtspress.com/webpage.php?ISBN=978-1-4327-4936-1

Email Contact: http://www.prlog.org/email-contact.html?id=10504152

Genre: COMPUTERS / Intelligence (AI) & Semantics

About the Author :-

Randall Scott is a retired Army Captain, Computer Scientist, and Assistant Professor. He holds a BS degree in Computer Engineering from Syracuse University and a MS degree in Computer Science (Software Engineering) from the Naval Postgraduate School.

Scott has served as a Tactical Communications Engineer, Systems Engineer, Software Security Expert, and much more. He lives in Martinez, Georgia.

About Outskirts Press, Inc. :-

Outskirts Press, Inc. offers full-service, custom self-publishing and book marketing services for authors seeking a cost-effective, fast, and flexible way to publish and distribute their books worldwide while retaining all their rights and full creative control. Available for authors globally at www.outskirtspress.com and located on the outskirts of Denver, Colorado, Outskirts Press represents the future of book publishing, today.

About the Book:-

A Guide to Artificial Intelligence with Visual Prolog by Randall Scott is available worldwide on book retailer websites such as Amazon and Barnes & Noble for a suggested retail price of $25.95.

ISBN: 9781432749361 Format: 6.14 x 9.21 paperback SRP: $25.95

Prolog – which stands for “programming with logic” -is one of the most effective languages with a unique approach for building AI applications . Using Prolog you define a problem with logical Rules, and then set the computer loose on it instead of writing a program that spells out exactly how to solve a problem. This paradigm shift from Procedural to Declarative programming makes Prolog ideal for applications involving AI, logic, language parsing, computational linguistics, and theorem-proving. Now, Visual Prolog (available as a free download) offers even more with its powerful Graphical User Interface (GUI), built-in Predicates, and rather large provided Program Foundation Class (PFC) libraries. A Guide to Artificial Intelligence with Visual Prolog is an excellent introduction to both Prolog and Visual Prolog. Designed for newcomers to Prolog with some conventional programming background (such as BASIC, C, C++, Pascal, etc.), Randall Scott proceeds along a logical, easy-to-grasp path as he explains the beginnings of Prolog, classic algorithms to get you started, and many of the unique features of Visual Prolog. Readers will also gain key insights into application development, application design, interface construction, troubleshooting, and more. In addition, there are numerous sample examples to learn from, copious illustrations and information on helpful resources. A Guide to Artificial Intelligence with Visual Prolog is less like a traditional textbook and more like a workshop where you can learn at your own pace – so you can start harnessing the power of Visual Prolog for whatever your mind can dream up. Deftly constructed at 190 pages, A Guide to Artificial Intelligence with Visual Prolog is being aggressively promoted to appropriate markets with a focus on the COMPUTERS / Intelligence (AI) & Semantics category. With U.S. wholesale distribution through Ingram and Baker & Taylor, and pervasive online availability through Amazon, Barnes & Noble and elsewhere, A Guide to Artificial Intelligence with Visual Prolog meets consumer demand through both retail and library markets with a suggested retail price of $25.95. Additionally, A Guide to Artificial Intelligence with Visual Prolog can be ordered by retailers or wholesalers for the maximum trade discount price set by the author in quantities of ten or more from the Outskirts Press wholesale online bookstore at www.outskirtspress.com/buybooks

For more information or to contact the author, visit www.outskirtspress.com/aguidetoartificialintelligence

[1] Overview of AI, Agent Architectures : Video Lecture 1, Video Lecture 2

[2] Uninformed Search, Form teams : Video Lecture 3, Video Lecture 4

[3] Informed Search: Video Lecture 5

[4] Game Playing: Video Lecture 6

[5] Video Game AI, Logic: Video Lecture 8, Video Lecture 9

[6] Predicate Calculus, Inference: Video Lecture 10, Video Lecture 11

[7] Prolog Review: Video Lecture 12, Video Lecture 13

[8] Semantic Networks, Knowledge Representation: Video Lecture 14

[9] Uncertainty: Video Lecture 15, Video Lecture 16

[10] Intro Bayesian Networks, Intro Machine Learning: Video Lecture 17, Video Lecture 18

[11] Decision Trees: Video Lecture 19

[12] Neural Networks: Video Lecture 20, Video Lecture 21

[13] Genetic Algorithms, Information Retrieval: Video Lecture 22, Video Lecture 23

Natural Language Processing:

Lecture 1:

Topics:Logistics, Goals Of The Field Of NLP, Is The Problem Just Cycles?, Why NLP Is Difficult? The Hidden Structure Of Language, Why NLP Is Difficult: Newspaper Headlines, Machine Translation, Machine Translation History, Centauri/Arcturan Example

Video: Lecture 1.

Lecture 2:

Topics: Questions That Linguistics Should Answer, Machine Translation (MT), Probabilistic Language Models, Evaluation, Sparsity, Smoothing, How Much Mass To Withhold?

Video: Lecture 2

Lecture 3:

Topics: Finish Smoothing From Last Lecture, Kneser-Ney Smoothing, Practical Considerations, Machine Translation (Lecture 3), Tokenization (Or Segmentation), Statistical MT Systems, IBM Translation Models

Video: Lecture 3.

Lecture 4:

Topics: Review Statistical Mt, Model 1, The Em Algorithm, Em And Hidden Structure, Em Algorithm Demonstration In Excel Spreadsheet, Assignment 1

Video: Lecture 4.

Lecture 5:

Topics: IBM Model 1-2 (Review), IBM Model 3, IBM Model 4, IBM Model 5, Mt Evaluation, Bleu Evaluation Metric, A Complete Translation System, Flaws Of Word-Based Mt, Phrased-Based Stat-Mt

Video: Lecture 5.

Lecture 6:

Topics: Continue Of Machine Translation, Syntax-Based Model, Information Extraction & Named Entity Recognition, Information Extraction, Named Entity Extraction, Precision And Recall, Naive Bayes Classifiers

Video: Lecture 6.

Lecture 7:

Topics: Continue Of Naive Bayes Classifier, Joint V.S. Conditional Models, Features, Examples, Feature-Based Classifiers, Comparison To Naïve-Bayes, Building A Maxent Model

Video: Lecture 7.

Lecture 8:

Topics: Details Of Maxent Model, Maxent Examples, Convexity, Feature Interaction, Classification, Smoothing, Inference In Systems

Video: Lecture 8.

Lecture 9:

Topics: MEMM, Hmm Pos Tagging Model, Summary Of Tagging, NER, Information Extraction And Integration, Landscape Of IE Tasks, Machine Learning Methods, Relation Extraction, Clustering

Video: Lecture 9.

Lecture 10:

Topics: Parsing, Classical NLP Parsing, Two Views Of Linguistic Structure, Attachment Ambiguities, A Simple Prediction, What Is Parsing?, Top-Down Parsing, Bottom-Up Parsing, Parsing Of PCFGs

Video: Lecture 10

Lecture 11:

Topics: Chomsky Normal Form, Cocke-Kasami-Younger (CKY) Constituency Parsing, Extended CKY Parsing, Efficient CKY Parsing, Evaluating Parsing Accuracy, How Good Are PCFGs?, Improve PCFG Parsing Via Unlexicalized Parsing, Markovization

Video: Lecture 11.

Lecture 12:

Topics: Guest Lecturer: Dan Jurafsky, Syntactic Variations Versus Semantic Roles, Some Typical Semantic Roles, Two Solutions To The Difficulty Of Defining Semantic Roles, PropBank, FrameNet, Information Extraction Versus Semantic Role Labeling, Evaluation Measures, Parsing Algorithm, Combining Identification And Classification Models, Summary

Video: Lecture 12.

Lecture 13:

Topics: Lexicalized Parsing, Parsing Via Classification Decisions: Charniak (1997), Sparseness & The Penn Treebank, Complexity Of Lexicalized PCFG Parsing, Complexity Of Lexicalized PCFG Parsing, Overview Of Collins’ Model, Choice Of Heads, The Latest Parsing Results, Parsing And Search Algorithms

Video: Lecture 13.

Lecture 14:

Topics: Parsing As Search, Agenda-Based Parsing, What Can Go Wrong?, Search In Modern Lexicalized Statistical Parsers, Dependency Parsing, Naïve Recognition/Parsing, Discriminative Parsing, Discriminative Models

Video: Lecture 14.

Lecture 15:

Topics: Why Study Computational Semantics?, Precise Semantics. An Early Example: Chat-80, Programming Language Interpreter, Logic: Some Preliminaries, Compositional Semantics, A Simple DCG Grammar With Semantics, Augmented CFG Rules, Semantic Grammars

Video:  Lecture 15.

Lecture 16:

Topics: An Introduction To Formal Computational Semantics, Database/ Knowledgebase Interfaces, Typed Lambda Calculus, Types Of Major Syntactic Categories, Adjective And PP Modification, Why Things Get More Complex, Generalized Quantifiers, Representing Proper Nouns With Quantifiers, Questions With Answers!, How Could We Learn Such Representations?

Video: Lecture 16.

Lecture 17:

Topics: Lexical Semantics, Lexical Information And NL Applications, Polysemy Vs Homonymy, WordNet, Word Sense Disambiguation, Corpora Used For WSD Work, Evaluation, Lexical Acquisition, Vector-Based Lexical Semantics, Measures Of Semantic Similarity

Video: Lecture 17.

Lecture 18:

Topics: Question Answering Systems And Textual Inference, A Brief (Academic) History, Top Performing Systems, Answer Types In State-Of-The-Art QA Systems, Semantics And Reasoning For QA, The Textual Inference Task, Why We Need Sloppy Matching, QA Beyond TREC

Video: Lecture 18.

Note: If the links either expired or exhibit errors, please visit SEE program home page and navigate from there. Thanks.

Read Link compilation #4 here.

This course is provided by Stanford University SEE program. The Machine Learning course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Lecture 1:

Topics: The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning

Lecture 2:

Topics: An Application of Supervised Learning – Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations

Lecture 3:

Topics: The Concept of Underfitting and Overfitting, The Concept of Parametric Algorithms and Non-parametric Algorithms, Locally Weighted Regression, The Probabilistic Interpretation of Linear Regression, The motivation of Logistic Regression, Logistic Regression, Perceptron

Lecture 4:

Topics: Newton’s Method, Exponential Family, Bernoulli Example, Gaussian Example, General Linear Models (GLMs), Multinomial Example, Softmax Regression

Lecture 5:

Topics: Discriminative Algorithms, Generative Algorithms, Gaussian Discriminant Analysis (GDA), GDA and Logistic Regression, Naive Bayes, Laplace Smoothing

Lecture 6:

Topics: Multinomial Event Model, Non-linear Classifiers, Neural Network, Applications of Neural Network, Intuitions about Support Vector Machine (SVM), Notation for SVM, Functional and Geometric Margins

Lecture 7:

Topics: Optimal Margin Classifier, Lagrange Duality, Karush-Kuhn-Tucker (KKT) Conditions, SVM Dual, The Concept of Kernels

Lecture 8:

Topics: Kernels, Mercer’s Theorem, Non-linear Decision Boundaries and Soft Margin SVM, Coordinate Ascent Algorithm, The Sequential Minimization Optimization (SMO) Algorithm, Applications of SVM

Lecture 9:

Topics: Bias/variance Tradeoff, Empirical Risk Minimization (ERM), The Union Bound, Hoeffding Inequality, Uniform Convergence – The Case of Finite H, Sample Complexity Bound, Error Bound, Uniform Convergence Theorem & Corollary

Lecture 10:

Topics: Uniform Convergence – The Case of Infinite H, The Concept of ‘Shatter’ and VC Dimension, SVM Example, Model Selection, Cross Validation, Feature Selection

Lecture 11:

Topics: Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example – Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem

Lecture 12:

Topics: The Concept of Unsupervised Learning, K-means Clustering Algorithm, K-means Algorithm, Mixtures of Gaussians and the EM Algorithm, Jensen’s Inequality, The EM Algorithm, Summary

Lecture 13:

Topics: Mixture of Gaussian, Mixture of Naive Bayes – Text clustering (EM Application), Factor Analysis, Restrictions on a Covariance Matrix, The Factor Analysis Model, EM for Factor Analysis

Lecture 14:

Topics: The Factor Analysis Model,0 EM for Factor Analysis, Principal Component Analysis (PCA), PCA as a Dimensionality Reduction Algorithm, Applications of PCA, Face Recognition by Using PCA

Lecture 15:

Topics: Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA

Lecture 16:

Topics: Applications of Reinforcement Learning, Markov Decision Process (MDP), Defining Value & Policy Functions, Value Function, Optimal Value Function, Value Iteration, Policy Iteration

Lecture 17:

Topics: Generalization to Continuous States, Discretization & Curse of Dimensionality, Models/Simulators, Fitted Value Iteration, Finding Optimal Policy

Lecture 18:

Topics: State-action Rewards, Finite Horizon MDPs, The Concept of Dynamical Systems, Examples of Dynamical Models, Linear Quadratic Regulation (LQR), Linearizing a Non-Linear Model, Computing Rewards, Riccati Equation

Lecture 19:

Topics: Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)

Lecture 20:

Topics: Partially Observable MDPs (POMDPs), Policy Search, Reinforce Algorithm, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning

Reader Older Link Compilation #3 here.

With Data Mining techniques, it is possible to “mine” data pools for “hidden” knowledge. Data mining gives you a major competitive advantage in view of the key role played by knowledge and knowledge management in the development of future markets. So why not join us on the route from simple data archiving to automatic knowledge extraction!” – http://www.data-mining-tutorial.de

Important Days: November 18-19, 2009

Registration:

For students: There is not much information provided. But students should contact and check if there are any special offers.

Location:

Victors Residenz Hotel Leipzig
Georgiring 13
04103 Leipzig
Phone: +49 (0)341 68 660
FAX: +49 (0)341 68 66 899