Machine Learning Techniques

Unit I

Introduction: Learning, Types of Learning, Well-Defined Learning Problems, Designing a Learning System, History of ML, Introduction of Machine Learning Approaches — Artificial Neural Network, Clustering, Reinforcement Learning, Decision Tree Learning, Bayesian Networks, Support Vector Machine, Genetic Algorithm, Issues in Machine Learning, Data Science vs Machine Learning.

Unit II

Regression, Bayesian Learning & SVM: Linear Regression and Logistic Regression. Bayesian Learning: Bayes Theorem, Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm. Support Vector Machine: Types of Support Vector Kernels (Linear, Polynomial, Gaussian), Hyperplane (Decision Surface), Properties and Issues in SVM.

Unit III

Decision Tree & Instance-Based Learning: Decision Tree Learning Algorithm, Inductive Bias, Inductive Inference, Entropy and Information Theory, Information Gain, ID3 Algorithm, Issues in Decision Tree Learning. Instance-Based Learning: k-Nearest Neighbour, Locally Weighted Regression, Radial Basis Function Networks, Case-Based Learning.

Unit IV

Artificial Neural Networks & Deep Learning: Perceptrons, Multilayer Perceptron, Gradient Descent and Delta Rule, Multilayer Networks, Backpropagation Algorithm Derivation, Generalization, Unsupervised Learning — SOM Algorithm and its variants. Deep Learning: Convolutional Neural Networks, Types of Layers (Convolutional, Activation, Pooling, Fully Connected), 1D and 2D Convolution, Training of Network, Case Studies (Diabetic Retinopathy, Smart Speaker, Self-Driving Car).

Unit V

Reinforcement Learning & Genetic Algorithms: Introduction to RL, Learning Task, Learning Models — Markov Decision Process, Q-Learning (Function and Algorithm), Applications, Introduction to Deep Q-Learning. Genetic Algorithms: Components, GA Cycle of Reproduction, Crossover, Mutation, Genetic Programming, Models of Evolution and Learning, Applications.