Foundations And Applications Of Machine Learning (Bengali)

By Prof. Adway Mitra | IIT Kharagpur

Learners enrolled: 1106 | Exam registration: 20

ABOUT THE COURSE:

The course will cover basic concepts of Machine Learning - supervised and unsupervised learning, along with algorithms like K-nearest neighbors, Decision Trees, Perceptrons, Neural Networks, Linear Regression, K-means, Hierarchical Clustering etc. Each algorithm will be motivated by possible applications in different domains, especially text and images. The aim will be to convey the ideas and intuitions of the algorithms, avoiding mathematical details. The medium of instruction will be in Bengali.

INTENDED AUDIENCE: Undergraduate students, teachers and graduate job aspirants from West Bengal

PREREQUISITES: mathematics of 10+2 or college level

INDUSTRY SUPPORT: AI/Data Science industry

Summary

Course Status :	Completed
Course Type :	Elective
Language for course content :	English
Duration :	8 weeks
Category :	Computer Science and Engineering
Credit Points :	2
Level :	Undergraduate
Start Date :	24 Jul 2023
End Date :	15 Sep 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	21 Aug 2023
Exam Date :	24 Sep 2023 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Motivations of AI/ML, Feature-based representation, Supervised and unsupervised learning

Week 2: K-nearest neighbor, Decision Trees with examples

Week 3: Linear Algebra and Probability Revision, Bayesian Classifier, Linear Classifier

Week 4: Optimization, Max-margin, Regression, Neural Networks

Week 5: Practical Issues with Supervised Learning and their solutions

Week 6: Unsupervised Learning

Week 7: Various Applications of the above concepts in different domains

Week 8: Issues of ethics, fairness, interpretability etc.

Books and references

Machine Learning, Tom Mitchell, McGraw Hill, 1997

Instructor bio

Prof. Adway Mitra

IIT Kharagpur

Prof. Adway Mitra is an assistant professor at the Centre of Excellence in AI. He works in the domain of Machine Learning and its applications in climate, remote sensing and modeling of physical processes. He designed and launched the course “Machine Learning for Earth System Sciences” in IIT Kharagpur, that is offered in every Spring Semester since 2020.

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: 24 September 2023 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 will not be dispatched.

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

- NPTEL team