Machine Learning for Engineering and Science Applications

IIT Madras, , Prof. Prof. GanapathyProf. Balaji Srinivasan

Updated On 02 Feb, 19

Overview

Recent applications of machine learning have exploded due to cheaply available computational resources as well as wide availability of data. Machine Learning (ML) techniques provides a set of tools that can automatically detect patterns in data which can then be utilized for predictions and for developing models. Developments in ML algorithms and computational capabilities have now made it possible to scale engineering analysis, decision making and design rapidly. This, however, requires an engineer to understand the limits and applicability of the appropriate ML algorithms. This course aims to provide a broad overview of modern algorithms in ML, so that engineers may apply these judiciously. Towards this end, the course will focus on broad heuristics governing basic ML algorithms in the context of specific engineering applications. Matlab will be used in this course but students will also be trained to implement these methods utilizing open source packages such as TensorFlow.

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Lecture 1: Introduction to the Course History of Artificial Intelligence

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Lecture Details

Course Details

COURSE LAYOUT

Week 1: Mathematical Basics 1 Introduction to Machine Learning, Linear Algebra

Week 2: Mathematical Basics 2 - Probability

Week 3: Computational Basics Numerical computation and optimization, Introduction to Machine learning packages

Week 4: Linear and Logistic Regression Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications

Week 5: Neural Networks Multilayer Perceptron, Backpropagation, Applications

Week 6: Convolutional Neural Networks 1 CNN Operations, CNN architectures

Week 7: Convolutional Neural Networks 2 Training, Transfer Learning, Applications

Week 8: Recurrent Neural Networks RNN, LSTM, GRU, Applications

Week 9: Classical Techniques 1 Bayesian Regression, Binary Trees, Random Forests, SVM, Nave Bayes, Applications

Week 10: Classical Techniques 2 k-Means, kNN, GMM, Expectation Maximization, Applications

Week 11: Advanced Techniques 1 Structured Probabilistic Models, Monte Carlo Methods

Week 12: Advanced Techniques 2 Autoencoders, Generative Adversarial Network

Thanks to the support from MathWorks, enrolled students have access to MATLAB for the duration of the course.