Syllabus

Module-1: [7 Hours]
Introduction to Machine Learning: Types of machine learning, Supervised learning: Regression, Classification, Unsupervised learning: Clustering, Basic concepts in machine learning: Bias, Variance, Training, testing, Model Fitting.

Module-2: [12 Hours]
Bayesian Decision Theory: Likelihood, Prior, Posterior, Minimum Error Rate Classification, Classifier, Discriminant Functions, Decision Surface, Discriminant Functions for Normal Density, Binary Feature. Maximum likelihood Estimation: Gaussian with unknown mean and unknown variance. Bayesian Parameter Estimation of Univariate Case of Gaussian Mixture Models and EM algorithm, GMMs.

Module-3: [5 Hours]
Component Analysis and Discriminants: Principal Component Analysis (PCA), linear discriminant analysis (LDA).

Module-4: [10 Hours]
Different Learning Techniques: Linear Regression and Parameter Estimation, Fundamentals of Neural Network, Support Vector Machine (SVM): Kernel functions, Optimal Hyperplane for linearly classifiable patterns, Optimal Hyperplane for non-separable patterns, SVMs for classification, K-means clustering,

Module-5: [4 Hours]
Evaluation Measures: Confusion Matrix, Accuracy, Precision, Recall, Specificity, ROC.

To understand the machine learning algorithms.

To design and development of the machine learning algorithms for solution of different problems.

1. To understand the fundamental concepts of machine learning techniques
2. To study and apply Bayesian Decision Theory and Parameter Estimation techniques.
3. To understand and apply different machine learning techniques
4. To study and analyse different techniques for dimensionality reduction of features
5. To understand and analyse evaluation measures for machine learning models

S. Haykin, Neural Networks - A Comprehensive Foundation, Peasrson Education, India

C. M. Bishop, Pattern Recognition and Machine Learning, Springer , 2013

Tom M. Mitchell, Machine Learning, McGraw Hill Education (India) , 2013

Ethem Alpaydin, Introduction to Machine Learning, MIT Press , 2nd edition, 2010.