ABOUT THE COURSE:This is an introductory course on Machine Learning (ML) that is offered to undergraduate and graduate students. The contents are designed to cover both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets. The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training and validation, testing, and evaluation with quantitative performance comparisons. Each week’s theory contents will be accompanied with a tutorial on python.INTENDED AUDIENCE: Senior UG and PG StudentsPREREQUISITES: Mandatory Prerequisites:1. Programming (Python)2.Matrix calculus3.Probability Theory Desirable Prerequisites:1. Linear AlgebraINDUSTRY SUPPORT: As of now, almost every company/industry requires AI/ML. A very short list is: Amazon; Apple; Google; Meta; Microsoft; IBM; NVIDIA; Qualcomm; TCS; Adobe; GE; Wipro
Machine Learning (ML) in Hindi
Indraprastha Institute of Information Technology Delhi and NPTEL via Swayam
Overview
Syllabus
Week 1: Introduction to Machine Learning; Review of Probability Theory; Review of Linear AlgebraWeek 2:Review of Linear Algebra continued; Linear Regression; k-Nearest Neighbors Regression; Kernel Regression + (Tutorial-1: Hands On Python Examples)Week 3: Continuation-Review of Linear Algebra continued; Linear Regression; k-Nearest Neighbors Regression; Kernel Regression + (Tutorial-1: Hands On Python Examples),Week 4:Logistic Regression + (Tutorial-2: Hands On Python Examples)Week 5:Multilayer Perceptron (MLP)/NN and Optimization + (Tutorial-3: Hands On Python Examples)Week 6:Practical Machine Learning: Bias-Variance; Training/Testing; Overfitting; Cross-Validation; Occam's razor; Regularization and Model Selection (Tutorial-3: Hands On Python Examples continued)Week 7:Support Vector Machines; Radial Basis Functions and Kernel SVMs + (Tutorial-4: Hands On Python Examples)Week 8:Continuation- Support Vector Machines; Radial Basis Functions and Kernel SVMs + (Tutorial-4: Hands On Python Examples)Week 9:Naïve Bayes Classification; Decision Tree & Random Forests; Bagging & Boosting + (Tutorial-5: Hands On Python Examples)Week 10:Clustering: K-means/Kernel K-means, K-NN classifier; Spectral Clustering; Mixture of Gaussians; Dimensionality Reduction: PCA and kernel PCA+ (Tutorial-6: Hands On Python Examples)Week 11:Continuation- Clustering: K-means/Kernel K-means, K-NN classifier; Spectral Clustering; Mixture of Gaussians; Dimensionality Reduction: PCA and kernel PCA+ (Tutorial-6: Hands On Python Examples)
Week 12:Introduction to Deep Learning: CNN for Image Classification and Autoencoders + (Tutorial-7: Hands On Python Examples)
Week 12:Introduction to Deep Learning: CNN for Image Classification and Autoencoders + (Tutorial-7: Hands On Python Examples)
Taught by
Prof. Anubha Gupta
