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IT-802 (A) Machine Learning Notes

5 notes for Machine Learning, unit by unit.

Unit 1 · Short notes Introduction Introduction · Examples of various Learning Paradigms · Perspectives and Issues · Concept Learning · Version Spaces · Finite and Infinite Hypothesis Spaces
Unit 2 · Short notes Supervised Learning Algorithms Learning a Class from Examples · Linear, Non‑linear, Multi‑class and Multi‑label classification · Decision Trees: ID3, Classification and Regression Trees (CART) · Regression: Linear Regression, Multiple Linear Regression, Logistic Regression · Neural Networks: Introduction, Perceptron, Multilayer Perceptron · Support vector machines: Linear and NonLinear, Kernel Functions
Unit 3 · Short notes Ensemble Learning Ensemble Learning Model Combination Schemes · Voting · Error‑Correcting Output Codes · Bagging: Random Forest Trees · Boosting: Adaboost · Stacking
Unit 4 · Short notes Unsupervised Learning Introduction to clustering · Hierarchical: AGNES, DIANA · Partitional: K‑means clustering, KMode Clustering · Self‑Organizing Map · Expectation Maximization · Gaussian Mixture Models
Unit 5 · Short notes Probabilistic Learning Bayesian Learning · Bayes Optimal Classifier · Naïve Bayes Classifier · Bayesian Belief Networks · Mining Frequent Patterns

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