Vipul Arora

Introduction to Machine Learning - EE952 (eMasters)

Vipul Arora
Department of Electrical Engineering, IIT Kanpur

TAs

Course Objectives:

This course aims to introduce the students to machine learning (ML) techniques used for various engineering applications. The lectures will focus on mathematical principles, and there will be coding based assignments for implementation, introducing students to tools such as sklearn and keras.

Pre-requisites:

There are no official pre-requisites for the course. But you may brush up your knowledge of the basics of linear algebra and probability theory.

Topics:

Lecture wise topics

Sr. No. Broad Title Topics No. of Hours
1 Introduction and Preliminaries 1. Classification, Regression, Reinforcement Learning 3
    2. Evaluation measures: confusion matrix, accuracy, precision, recall, F-measure, MSE, MAE  
    3. Basic probability theory: discrete and continuous random variables, joint pdf, conditional pdf, marginal pdf, Bayes theorem, independence, Expectation values, sampling, standard distributions  
2 Linear Models 4. Decision Theory 3
    5. Linear Regression: least square solution, min-norm solution, ridge regression, gradient descent, data normalization  
    6. Linear Classification: multi-class classification, multi-label classification  
3 Supervised Learning 7. Neural Networks for Regression and Classification: weights, non-linearities, loss functions, training 4
    8. Neural Network optimization: gradient descent methods, risk minimization, hyperparameter tuning, regularization,  
    9. Convolutional NNs and Recurrent NNs  
4 Unsupervised learning 10. Clustering: K-means 3
    11. Distribution learning: Maximum likelihood estimation, Gaussian pdf, Gaussian Mixture Models  
    12. Dimensionality reduction and visualization: Principal component analysis  
5 Time series processing 13. Time series analysis 2
    14. Dynamic time warping  
6 ML at scale 15. Parameter tuning 1
    16. Model selection  
    17. Validation and testing  

References: