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

[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.

• IEEE Transactions on Knowledge and Data Engineering: IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.
• IEEE Transactions on Pattern Analysis and machine intelligence :IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence.
• Knowledge and Information Systems
• Data Mining and Knowledge Discovery: The premier technical publication in the field, Data Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.

Industry publications:

• Information Management
• Intelligence Enterprise

Read older Link Compilation #2

2) Dataspace: DataSpace is a web services based infrastructure for exploring, analyzing, and mining remote and distributed data. This site describes DataSpace protocols, DataSpace applications, and open source DataSpace servers and clients.

3) Sql Server Datamining: This site has been designed by the SQL Server Data Mining team to provide the SQL Server community with access to and information about our exciting data mining features.

4) KDnuggets: Data Mining Community’s Top Resource Since 1997 for Data Mining and Analytics news, software, jobs, courses, data, and more.

Read older Link Compilation # 1

1) Google’s Techtalk on  Supporting Scalable Online Statistical Processing:

2) Apache Lucene Mahout :

Mahout’s goal is to build scalable, Apache licensed machine learning libraries. Initially, we are interested in building out the ten machine learning libraries detailed inhttp://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf using Hadoop. While these algorithms are our initial focus, we welcome contributions of other machine learning approaches.

3) Kurt’s Recomended Datamining books: A must visit.

4) MIT Reality Mining:

Reality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.