1. Introduction : Scope of the Course, Introduction to AI, Brief review of History of AI, Related fields. (Chapter - 1) 2. Introduction to Artificial Neural Networks : Biological Neurons and Biological Neural Networks, Artificial Neural Networks, Activation Functions, Perceptron NN, Multilayer Perceptron NN, Back-propagation Neural Networks, Training Methods, Basic definition of supervised and unsupervised Learning. (Chapter - 2) 3. Introduction to Machine Learning : Introduction (Different Types of Learning) Hypothesis Space, Inductive Bias, Evaluation and Cross Validation. (Chapter - 3) 4. Main Algorithms used in Machine Learning : Linear Regression, Decision Trees, Learning Decision Trees, K-nearest Neighbour, Collaborative Filtering, Overfitting, Dimensionality Reduction Technique : Feature Selection, Feature Extraction. (Chapter - 4) 5. Logistic Regression and Support Vector Machine Logistic Regression, Introduction to Support Vector Machine, The Dual Formation, Maximum Margin with Noise, Nonlinear SVM and Kernel Function, SVM : Solution to the Dual Problem. (Chapter - 5) 6. Advanced Learning methods and Clustering : Introduction to Clustering, K-means Clustering, Agglomerative Hierarchical Clustering, Basics of Semi-Supervised and Reinforcement Learning, Introduction to Deep Learning. (Chapter - 6) 7. Fuzzy Logic : Introduction , Conventional set vs fuzzy set, Operations of fuzzy set , Membership function, Fuzzy rules, Fuzzy inference, De-fuzzification, Application for control. (Chapter - 7) 8. Genetic Algorithm : Introduction, Comparison with traditional optimisation Technique, Steps for GA, reproduction, Crossover, Mutation, Termination parameter of GA, Application. (Chapter - 8)
