Syllabus Artificial Intelligence and Machine Learning (CS3491) UNIT I PROBLEM SOLVING Introduction to AI - AI Applications - Problem solving agents - search algorithms - uninformed search strategies - Heuristic search strategies - Local search and optimization problems - adversarial search - constraint satisfaction problems (CSP). (Chapters - 1, 2, 3, 4, 5, 6) UNIT II PROBABILISTIC REASONING Acting under uncertainty - Bayesian inference - naïve bayes models. Probabilistic reasoning - Bayesian networks - exact inference in BN - approximate inference in BN - causal networks. (Chapter - 7) UNIT III SUPERVISED LEARNING Introduction to machine learning - Linear Regression Models : Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function - Probabilistic discriminative model - Logistic regression, Probabilistic generative model - Naive Bayes, Maximum margin classifier - Support vector machine, Decision Tree, Random forests. (Chapter - 8) UNIT IV 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 - 9) UNIT V NEURAL NETWORKS Perceptron - 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 - 10)
