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