1.Introduction to Machine Learning : Overview of Human Learning and Machine Learning, Types of Machine Learning, Applications of Machine Learning , Tools and Technology for Machine Learning. (Chapter - 1) 2.Overview of Probability : Statistical tools in Machine Learning, Concepts of probability, Random variables, Discrete distributions, Continuous distributions, Multiple random variables, Central limit theorem, Sampling distributions, Hypothesis space and inductive bias, Evaluation and Cross Validation, Hypothesis testing, Monte Carlo Approximation. (Chapter - 2) 3.Bayesian Concept Learning : Importance of Bayesian methods, Bayesian theorem, Bayes’ theorem and concept learning, Bayesian Belief Network. (Chapter - 3) 4.Classification and Regression : Supervised Learning vs Unsupervised Learning, Supervised Learning, Classification Model, Learning steps, Classification algorithms, Clustering, Association rules, Linear Regression, Multivariate Regression, Logistic Regression. (Chapter - 4) 5.Neural Networks : Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation. (Chapter - 5) 6.Foundations of neural networks and deep learning, Techniques to improve neural networks : Regularization and optimizations, hyperparameter tuning and deep learning frameworks (Tensorflow and Keras.), Convolutional Neural Networks, its applications, Recurrent Neural Networks and its applications. (Chapter - 6) 7.Deep Learning - more to know : Generative Adversarial Networks, Deep Reinforcement Learning, Adversarial Attacks. (Chapter - 7)
