Introduction to Machine Learning

Introduction to Machine Learning. Instructor: Prof. Sudeshna Sarkar, Department of Computer Science and Engineering, IIT Kharagpur. This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naive Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions. (from

Lecture 02 - Different Types of Learning

This lecture presents different types of machine learning algorithms viz. Supervised, Unsupervised, Semi-supervised and Reinforcement Learning. It also introduces different types of features viz. categorical, ordinal, integer valued and real valued. Some essential terminology of machine learning has also been defined.

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Lecture 01 - Introduction
Lecture 02 - Different Types of Learning
Lecture 03 - Hypothesis Space and Inductive Bias
Lecture 04 - Evaluation and Cross-Validation
Lecture 05 - Linear Regression
Lecture 06 - Introduction to Decision Trees
Lecture 07 - Learning Decision Tree
Lecture 08 - Overfitting
Lecture 09 - Python Exercise on Decision Tree and Linear Regression
Lecture 10 - K-Nearest Neighbour
Lecture 11 - Feature Selection
Lecture 12 - Feature Extraction
Lecture 13 - Collaborative Filtering
Lecture 14 - Python Exercise on K-Nearest Neighbor and Principal Components Analysis
Lecture 15
Lecture 16 - Bayesian Learning
Lecture 17 - Naive Bayes
Lecture 18 - Bayesian Network
Lecture 19 - Python Exercise on Naive Bayes
Lecture 20 - Logistic Regression
Lecture 21 - Introduction to Support Vector Machine
Lecture 22 - SVM: The Dual Formulation
Lecture 23 - SVM: Maximum Margin with Noise
Lecture 24 - Nonlinear SVM and Kernel Function
Lecture 25 - SVM: Solution to the Dual Problem
Lecture 26 - Python Exercise on SVM
Lecture 27 - Introduction to Neural Networks
Lecture 28 - Multilayer Neural Network
Lecture 29 - Neural Network and Backpropagation Algorithm
Lecture 30 - Deep Neural Network
Lecture 31 - Python Exercise on Neural Network
Lecture 32 - Introduction to Computational Learning Theory
Lecture 33 - Sample Complexity: Finite Hypothesis Space
Lecture 34 - VC Dimension
Lecture 35 - Introduction to Ensembles
Lecture 36 - Bagging and Boosting
Lecture 37 - Introduction to Clustering
Lecture 38 - K-means Clustering
Lecture 39 - Agglomerative Hierarchical Clustering
Lecture 40 - Python Exercise on K-means Clustering