Introduction to Machine Learning

Start Date
22/01/2018

End Date
13/04/2018

No. of
Enrollments
1118 students

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syllabus uploaded

Created by

Balaraman Ravindran
IIT Madras
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Course Language
English
Course Type
Scheduled
Video transcripts
English
Course Category
Engineering
Learning Path
Undergraduate
Course Length
30 Hours
Weekly time commitments
20 Hours
Course Completion
Yes, after passing all tests.
Exam Date
To be announced
Credits
0

70

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0

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0

Assignment

0

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Weekly Reading list

Overview

For certification please click here

 

Last date for enrollment: January 22, 2018

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.

To access the content, please enroll in the course.

Faculty

Balaraman Ravindran


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 (Recap), Linear Algebra (Recap), Convex Optimization (Recap)"
Week 1:  Introduction: Statistical Decision Theory - Regression, Statistical Decision Theory -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, LDA
Week 4   Perceptron, SVM
Week 5   Neural Networks - Introduction, Early Models, Perceptron Learning, Neural Networks - Backpropagation,               Neural Networks - Initialization, Training & Validation, Parameter Estimation
Week 6    Decision Trees, Regression Tree, Decision Trees - Stopping Criterion & Pruning, Loss functions, Decision Trees - Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability, Example, Evaluation Measures-1"
Week 7    Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Ensemble Methods - 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 and TD learning,Solution Methods and Applications)
 
 
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; Afternoon session: 2pm to 5pm
  • Exam for this course will be available in one session on both 28 and 29 April. The exact session it will be available in (FN/AN) - we shall inform by first week of January 2018.
  • 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.

FAQs

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