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Swayam Central

Introduction to Machine Learning

By Prof. Sudeshna Sarkar   |   IIT Kharagpur
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.

INTENDED AUDIENCE Elective course for UG, PG, BE, ME, MS, M.Sc, PhD
PRE-REQUISITES Basic programming skills (in Python), algorithm design, basics of probability & statistics
INDUSTRY SUPPORT Data science companies and many other industries value machine learning skills.

SUMMARY

Course Status : Upcoming
Course Type : Elective
Duration : 8 weeks
Start Date : 29 Jul 2019
End Date : 20 Sep 2019
Exam Date : 29 Sep 2019
Category :
  • Computer Science and Engineering
  • Level : Undergraduate
    This is an AICTE approved FDP course

    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

    BOOKS AND REFERENCES

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

    INSTRUCTOR BIO


    Prof. 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 sered 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.

    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: 29th September 2019, 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 6 assignments out of the total 8 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 Kharagpur. It will be e-verifiable at nptel.ac.in/noc.
    • Only the e-certificate will be made available. Hard copies are being discontinued from July 2019 semester and will not be dispatched

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