Introduction to Machine Learning. Instructor: Dr. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras.
FREE
This course includes
Units & Quizzes
86
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Certificate of Completion
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms (from nptel.ac.in)
Course Currilcum

 Lecture 01 – A Brief Introduction to Machine Learning Unlimited
 Lecture 02 – Supervised Learning Unlimited
 Lecture 03 – Unsupervised Learning Unlimited
 Lecture 04 – Reinforcement Learning Unlimited

 Lecture 05 – Probability Basics 1 Unlimited
 Lecture 06 – Probability Basics 2 Unlimited

 Lecture 07 – Linear Algebra 1 Unlimited
 Lecture 08 – Linear Algebra 2 Unlimited

 Lecture 09 – Statistical Decision Theory: Regression Unlimited
 Lecture 10 – Statistical Decision Theory: Classification Unlimited
 Lecture 11 – BiasVariance Unlimited

 Lecture 12 – Linear Regression Unlimited
 Lecture 13 – Multivariate Regression Unlimited

 Lecture 14 – Subset Selection 1 Unlimited
 Lecture 15 – Subset Selection 2 Unlimited
 Lecture 16 – Shrinkage Methods Unlimited
 Lecture 17 – Principal Components Regression Unlimited
 Lecture 18 – Partial Least Squares Unlimited

 Lecture 19 – Linear Classification Unlimited
 Lecture 20 – Logistic Regression Unlimited
 Lecture 21 – Linear Discriminant Analysis 1 Unlimited
 Lecture 22 – Linear Discriminant Analysis 2 Unlimited
 Lecture 23 – Linear Discriminant Analysis 3 Unlimited
 Lecture 24 – Weka Tutorial Unlimited

 Lecture 25 – Optimization Unlimited

 Lecture 26 – Perceptron Learning Unlimited
 Lecture 27 – Support Vector Machines – Formulation Unlimited
 Lecture 28 – Support Vector Machines – Interpretation and Analysis Unlimited
 Lecture 29 – Support Vector Machines for Linearly Nonseparable Data Unlimited
 Lecture 30 – SVM Kernels Unlimited
 Lecture 31 – SVM – Hinge Loss Formulation Unlimited

 Lecture 32 – Early Methods Unlimited
 Lecture 33 – Backpropagation I Unlimited
 Lecture 34 – Backpropagation II Unlimited
 Lecture 35 – Initialization, Training and Validation Unlimited

 Lecture 36 – Maximum Likelihood Estimate Unlimited
 Lecture 37 – Priors and the MAP Estimate Unlimited
 Lecture 38 – Bayesian Parameter Estimation Unlimited

 Lecture 39 – Decision Trees: Introduction Unlimited
 Lecture 40 – Regression Trees Unlimited
 Lecture 41 – Stopping Criteria and Pruning Unlimited
 Lecture 42 – Decision Trees for Classification – Loss Functions Unlimited
 Lecture 43 – Categorical Attributes Unlimited
 Lecture 44 – Multiway Splits Unlimited
 Lecture 45 – Missing Values, Imputation and Surrogate Splits Unlimited
 Lecture 46 – Instability, Smoothness and Repeated Subtrees Unlimited
 Lecture 47 – Decision Trees: Tutorial Unlimited

 Lecture 48 – Evaluation and Evaluation Measures I Unlimited
 Lecture 49 – Bootstrapping and Cross Validation Unlimited
 Lecture 50 – 2 Class Evaluation Measures Unlimited
 Lecture 51 – The ROC Curve Unlimited
 Lecture 52 – Minimum Description Length and Exploratory Analysis Unlimited

 Lecture 53 – Introduction to Hypothesis Testing Unlimited
 Lecture 54 – Hypothesis Testing: Basic Concepts Unlimited
 Lecture 55 – Sampling Distributions and the Z Test Unlimited
 Lecture 56 – Student’s TTest Unlimited
 Lecture 57 – The Two Samples and Paired Sample TTests Unlimited
 Lecture 58 – Confidence Intervals Unlimited

 Lecture 59 – Bagging, Committee Machines and Stacking Unlimited
 Lecture 60 – Boosting Unlimited
 Lecture 61 – Gradient Boosting Unlimited
 Lecture 62 – Random Forests Unlimited

 Lecture 63 – Naive Bayes Unlimited
 Lecture 64 – Bayesian Networks Unlimited
 Lecture 65 – Undirected Graphical Methods: Introduction and Factorization Unlimited
 Lecture 66 – Undirected Graphical Methods: Potential Functions Unlimited
 Lecture 67 – Hidden Markov Models Unlimited
 Lecture 68 – Variable Elimination Unlimited
 Lecture 69 – Belief Propagation Unlimited

 Lecture 70 – Partitional Clustering Unlimited
 Lecture 71 – Hierarchical Clustering Unlimited
 Lecture 72 – Threshold Graphs Unlimited
 Lecture 73 – The BIRCH Algorithm Unlimited
 Lecture 74 – The CURE Algorithm Unlimited
 Lecture 75 – Density based Clustering Unlimited

 Lecture 76 – Gaussian Mixture Models Unlimited
 Lecture 77 – Expectation Maximization Unlimited
 Lecture 78 – Expectation Maximization (cont.) Unlimited

 Lecture 79 – Spectral Clustering Unlimited

 Lecture 80 – Learning Theory Unlimited

 Lecture 81 – Frequent Itemset Mining Unlimited
 Lecture 82 – The Apriori Property Unlimited

 Lecture 83 – Introduction to Reinforcement Learning Unlimited
 Lecture 84 – RL Framework and TD Learning Unlimited
 Lecture 85 – Solution Methods and Applications Unlimited

 Lecture 86 – Multiclass Classification Unlimited