Introduction to Machine Learning

Start Date
24/07/2017

End Date
15/09/2017

No. of
Enrollments
645 students

No course
syllabus uploaded

Created by

Sudeshna Sarkar
Indian Institute of Technology - Kaharagpur
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Course Language
English
Course Type
Scheduled
Video transcripts
Course Category
Engineering
Learning Path
Undergraduate
Course Length
0 Hours
Weekly time commitments
0 Hours
Course Completion
Exam Date
To be announced
Credits
0

75

Tutorials

0

Test

0

Assignment

0

Article

0

Weekly Reading list

Overview

ABOUT THE COURSE

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

 

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INTENDED AUDIENCE

 

  • Elective course
  • UG 
  • PG 
  • BE
  • ME
  • MS
  • MSc
  • PhD

 

PRE-REQUISITES

 

  • Basic programming skills (in Python), algorithm design, basics of probability & statistics

 

INDUSTRY SUPPORT – LIST OF COMPANIES/INDUSTRY THAT WILL RECOGNIZE/VALUE THIS ONLINE COURSE

Data science companies and many other industries value machine learning skills.

 

COURSE LAYOUT
Week 1: Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
Week 2: Linear regression, Decision trees, overfitting
Week 3: Instance based learning, Feature reduction, Collaborative filtering based recommendation
Week 4: Probability and Bayes learning
Week 5: Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
Week 6: Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
Week 7: Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
Week 8: Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model
SUGGESTED READING

1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
2. Introduction to Machine Learning Edition 2, by Ethem Alpaydin

To access the content, please enroll in the course.

Faculty

Sudeshna Sarkar


Sudeshna Sarkar is a Professor and currently the Head in the Department of Computer Science and Engineering at IIT Kharagpur. She completed her B.Tech. in 1989 from IIT Kharagpur, MS from University of California, Berkeley, and PhD from IIT Kharagpur in 1995. She served briefly in the faculty of IIT Guwahati and at IIT Kanpur before joining IIT Kharagpur in 1998. Her research interests are in Machine Learning, Natural Language Processing, Data and Text Mining.

The Teaching Assistants of this course are Anirban Santara and Ayan Das, both of whom are PhD students in Computer Science & Engineering Department, IIT Kharagpur. They will take active part in the course especially in running demonstration and programming classes as well as tutorial classes.

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