Syllabus Machine Learning (AL3451) UNIT I INTRODUCTION TO MACHINE LEARNING Review of Linear Algebra for machine learning; Introduction and motivation for machine learning; Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization, Bias variance trade-off. (Chapter - 1) UNIT II SUPERVISED LEARNING Linear Regression Models : Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models : Discriminant function - Perceptron algorithm, Probabilistic discriminative model - Logistic regression, Probabilistic generative model - Naïve Bayes, Maximum margin classifier - Support vector machine, Decision Tree, Random Forests (Chapter - 2) UNIT III ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING Combining multiple learners : Model combination schemes, Voting, Ensemble Learning - bagging, boosting, stacking, Unsupervised learning : K-means, Instance Based Learning : KNN, Gaussian mixture models and Expectation maximization. (Chapter - 3) UNIT IV NEURAL NETWORKS Multilayer perceptron, activation functions, network training - gradient descent optimization - stochastic gradient descent, error backpropagation, from shallow networks to deep networks - Unit saturation (aka the vanishing gradient problem) - ReLU, hyperparameter tuning, batch normalization, regularization, dropout. (Chapter - 4) UNIT V DESIGN AND ANALYSIS OF MACHINE LEARNING EXPERIMENTS Guidelines for machine learning experiments, Cross Validation (CV) and resampling - K-fold CV, bootstrapping, measuring classifier performance, assessing a single classification algorithm and comparing two classification algorithms - t test, McNemar’s test, K-fold CV paired t test. (Chapter - 5)
