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Courses » Introduction to Machine Learning

Introduction to Machine Learning

ABOUT THE COURSE

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. 

INTENDED AUDIENCE
This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD 

PRE-REQUISITES
We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well. 

INDUSTRY SUPPORT
Any company in the data analytics/data science/big data domain would value this course.

15716 students have enrolled already!!

COURSE INSTRUCTOR 



Prof. Balaraman Ravindran is currently an associate professor in Computer Science at IIT Madras. He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.
COURSE LAYOUT

Week 0:     Probability Theory, Linear Algebra, Convex Optimization - (Recap)
Week 1:     Introduction: Statistical Decision Theory - Regression, Classification, Bias
                  Variance
Week 2:     Linear Regression, Multivariate Regression, Subset Selection,
                  Shrinkage Methods, Principal Component Regression, Partial Least squares
Week 3      Linear Classification, Logistic Regression, Linear Discriminant Analysis
Week 4      Perceptron, Support Vector Machines
Week 5      Neural Networks - Introduction, Early Models, Perceptron Learning,
                  Backpropagation, Initialization, Training & Validation
                  Parameter Estimation - MLE, MAP, Bayesian Estimation
Week 6      Decision Trees, Regression Trees, Stopping Criterion & Pruning
                  Loss functions, Categorical Attributes, Multiway Splits, Missing Values
                  Decision Trees - Instability
                  Evaluation Measures
Week 7      Bootstrapping & Cross Validation, Class Evaluation Measures,
                  ROC curve, MDL
                  Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting
Week 8:     Gradient Boosting, Random Forests, Multi-class Classification
                  Naive Bayes, Bayesian Networks
Week 9:     Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
Week 10:   Partitional Clustering, Hierarchical Clustering, Birch Algorithm
                  CURE Algorithm, Density-based Clustering
Week 11:   Gaussian Mixture Models, Expectation Maximization
Week 12:   Learning Theory, Introduction to Reinforcement Learning
                  Optional videos (RL framework, TD learning, Solution Methods, Applications)


Recommended Text Books
1. The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani,
    Jerome H. Friedman (freely available online)
2. Pattern Recognition and Machine Learning, by Christopher Bishop (optional)
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CERTIFICATE EXAM
  • The exam is optional for a fee.
  • Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) : Morning session 9am to 12 noon;
  • Exam for this course will be available in one session on both 28 and 29 April.
  • Registration url: Announcements will be made when the registration form is open for registrations.
  • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published.

CERTIFICATE

  • Final score will be calculated as : 25% assignment score + 75% final exam score
  • 25% assignment score is calculated as 25% of average of Best 8 out of 12 assignments
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of NPTEL and IIT MADRAS. It will be e-verifiable at nptel.ac.in/noc.