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)
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.
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

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
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