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.
Overview
Syllabus
Introduction to Machine Learning.
Week 1 - Lecture 1 - Introduction to Machine Learning.
Week 1 Lecture 2 - Supervised Learning.
Week 1 Lecture 3 - Unsupervised Learning.
Week 1 Lecture 4 - Reinforcement Learning.
Week 2 Lecture 5 - Statistical Decision Theory - Regression.
Week 2 Lecture 6 - Statistical Decision Theory - Classification.
Week 2 Lecture 7 - Bias - Variance.
Week 2 Lecture 8 - Linear Regression.
Week 2 Lecture 9 - Multivariate Regression.
Week 3 Lecture 10 Subset Selection 1.
Week 3 Lecture 11 Subset Selection 2.
Week 3 Lecture 12 Shrinkage Methods.
Week 3 Lecture 13 Principal Components Regression.
Week 3 Lecture 14 Partial Least Squares.
Week 3 Lecture 15 Linear Classification.
Week 3 Lecture 16 Logistic Regression.
Week 3 Lecture 17 Linear Discriminant Analysis 1.
Week 3 Lecture 18 Linear Discriminant Analysis 2.
Week 3 Lecture 19 Linear Discriminant Analysis 3.
Week 4 Lecture 20 Perceptron Learning.
Week 4 Lecture 21 SVM - Formulation.
Week 4 Lecture 22 SVM - Interpretation & Analysis.
Week 4 Lecture 23 SVMs for Linearly Non Separable Data.
Week 4 Lecture 24 SVM Kernels.
Week 4 Lecture 25 SVM - Hinge Loss Formulation.
Week 5 Lecture 26 ANN I - Early Models.
Week 5 Lecture 27 ANN II - Backprogpogation I.
Week 5 Lecture 28 ANN III - Backpropogation II.
Week 5 Lecture 29 ANN IV - Initialization, Training & Validation.
MAXIMUM LIKELIHOOD ESTIMATE.
Week 5 Lecture 31 Parameter Estimation II - Priors & MAP.
Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation.
Week 6 Lecture 33 Decision Trees - Introduction.
Week 6 Lecture 34 Regression Trees.
Week 6 Lecture 35 Stopping Criteria & Pruning.
Week 6 Lecture 36 Decision Trees for Classification - Loss Functions.
Week 6 Lecture 37 Decision Trees - Categorical Attributes.
Week 6 Lecture 38 Decision Trees - Multiway Splits.
Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits.
Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees.
Week 6 Lecture 41 Decision Trees - Example.
Week 6 Lecture 42 Evaluation Measures 1.
Week 6 Lecture 43 Bootstrapping & Cross Validation.
Week 6 Lecture 44 - 2 Class Evaluation Measures.
Week 6 Lecture 45 - The ROC Curve.
Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis.
Week 7 Lecture 47 - Introduction to Hypothesis Testing.
Week 7 Lecture 48 - Basic Concepts.
Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test.
Week 7 Lecture 50 -STUDENT'S T-TEST.
Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests.
Week 7 Lecture 52 - Confidence Intervals.
Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking.
Week 8 Lecture 54 - Boosting.
Week 8 Lecture 55 - Gradient Boosting.
Week 8 Lecture 56 - Random Forests.
Week 8 Lecture 57 - Naive Bayes.
Week 9 Lecture 58 Bayesian Networks.
Week 9 Lecture 59 Undirected Graphical Models - Introduction.
Week 8 Lecture 60 Undirected Graphical Models - Potential Functions.
Week 9 Lecture 61 Hidden Markov Models.
Week 9 Lecture 62 Variable Elimination.
Week 9 Lecture 63 Belief Propagation.
Lecture 64 Multi-class Classification.
Week 10 Lecture 65 Partional Clustering.
Week 10 Lecture 66 Hierarchical Clustering.
Week 10 Lecture 67 Threshold Graphs.
Week 10 Lecture 68 The BIRCH Algorithm.
Week 10 Lecture 69 The CURE Algorithm.
Week 10 Lecture 70 Density Based Clustering.
Week 11 Lecture 71 Gaussian Mixture Models.
Week 11 Lecture 72 Expectation Maximization.
Week 11 Lecture 73 Expectation Maximization Continued.
Lecture 76 Spectral Clustering.
The Apriori Property.
Frequent Itemset Mining.
Lecture 79 Learning Theory.
Lecture 80 Introduction to Reinforcement Learning.
Lecture 81 - RL Framework and TD Learning.
Lecture 82 Solution Methods & Applications.
Week 6 Decision Trees Tutorial.
Week 4 Tutorial 4 - Optimization.
Week 3 Weka Tutorial.
Week 2 Tutorial 2 - Linear Algebra (2).
Week 2 Tutorial 2 - Linear Algebra (1).
Week 1 Tutorial 1 - Probability Basics (2).
Week 1 Tutorial 1 - Probability Basics (1).
Taught by
Machine Learning- Balaraman Ravindran
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Reviews
5.0 rating, based on 2 Class Central reviews
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I started on the wrong course for me but was supported greatly in the transition to something better suited! All the stuff are so lovely and I feel truly cared for by my teachers. I have learned so much already and have been inspired from being surrounded by so much passion and talent. There are so many opportunities to play and create in a range of ensembles, and I am happy here.
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Goutham Lmhe ML course by IIT is a comprehensive and well-structured program. The instructors are knowledgeable, and the course content is insightful, covering a wide range of topics. The practical assignments and real-world applications enhance the learning experience. However, at times, the pace could be intense for beginners. Overall, it's a valuable learning opportunity for those serious about delving into machine learning.
