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Introduction to Machine Learning

By Prof. Balaraman Ravindran   |   IIT Madras
Learners enrolled: 24373
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 
PREREQUISITES : 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.
Summary
Course Status : Ongoing
Course Type : Elective
Duration : 12 weeks
Start Date : 18 Jan 2021
End Date : 09 Apr 2021
Exam Date : 25 Apr 2021
Enrollment Ends : 01 Feb 2021
Category :
  • Computer Science and Engineering
  • Artificial Intelligence
  • Data Science
  • Programming
Level : Undergraduate/Postgraduate



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)

Books and references

  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)

Instructor bio

Prof. Balaraman Ravindran

IIT Madras
Prof. Balaraman Ravindran is currently an Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow . 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 certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 25 April 2021 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
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. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

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.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team


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