Course

Module-1

Mathematical concept review: Linear algebra and Probability, Introduction to Machine learning, Terminologies in machine learning, Types of machine learning, Linear Regression, Multi variate Regression, Bias Variance, Classification of Linear models, Bayesian Classifiers.

Module-2

Regularization, Hyper-parameter Tuning,Subset selection, shrinkage method, Dimensionality Reduction, Evaluation measures, Decision trees, Ensemble models–Bagging and Boosting, Random Forest.

Module-3

Support Vector Machines – Large margin classifiers, Nonlinear SVM, kernel functions Hyperplane, Perceptron Learning, Unsupervised Learning Algorithms: Dimensionality Reduction- Principal Component Analysis (PCA), Clustering – Hierarchical, Partitioned clustering: K-means, Basics of Neural Network.

• CO1 : Develop a good understanding of fundamental principles of machine learning. CO2 : Formulation of a Machine Learning problem.
• CO3 : Develop a model using supervised/unsupervised machine learning algorithms for classification/prediction/clustering.
• CO4 : Evaluate performance of various machine learning algorithms on various datasets of a domain.
• CO5 : Design and Concrete implementations of various machine learning algorithms to solve a given problem using languages such as Python.

• GilbertStrang, Linear Algebra and learning from data, Wellesley, Cambridge press,2019.
• Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press2012
• Tom Mitchell, Machine Learning, McGraw Hill,1997
• James, R.Tibshirani, An Introduction to Statistical Learning :with applications in R, Springer.
• Hastie, R. Tibshirani, Elements of Statistical Learning: Data mining, Inference and Prediction Springer.
• Andreas Muller and Sarah Guido, Introduction to Machine Learning with Python : A Guide for Data Scientists, Shroff/O’Reilly, 2016.