Machine Intelligence and Learning

Introduction to machine intelligence and intelligent agents; problem solving; knowledge representation and reasoning (logical and probabilistic); need for learning; basics of machine learning; Decision Trees; Rule-based models; linear learning models; Support Vector Machines; Artificial Neural Networks; Deep Learning; Probabilistic Modelling; Naive Bayes; Reinforcement Learning; Clustering; Feature Selection; Principal Component Analysis; Combining models; Philosophical issues in intelligence and learning. Substantive implementation assignments or a term project involving design of an intelligent learning-based system.