Educational Programs

UG Minor Program 2023

The course is specifically crafted for the advantage of second or third year undergraduate engineering students doing four year B Tech program in India. The course will commence in August 2023.


This minor program is designed for individuals aiming to unlock a world of opportunities through a comprehensive machine learning learning approach. Participants can acquire in-demand skills from the comfort of their home, progressing at their own pace. This program paves the way to becoming a sought-after professional in the thriving field of artificial intelligence.

Immerse yourself in an interactive and live learning experience administered by IIIT Hyderabad’s expert faculty members. Whether you’re interested in hands-on projects, collaboration with experienced assistants, or building a robust foundation in modern machine learning, this program is tailored for you.

Who can participate?

  • Students pursuing 4-year UG program in engineering/technology
  • Students should be in their second or third year of UG engineering.
  • Students should be studying in an AICTE recognised institution or a technical institution of repute in India.
  • Students should be willing to spare at least three hours every week for learning the course.

What makes this program unique?

  • B Tech Minor program in Modern Machine Learning
  • Live online lectures and hands-on sessions with personalised learning experience
  • Includes independent projects, quizzes and assignments.
  • Equal focus on foundation and practices, with discussions with eminent professionals

What is the qualifying criteria?

  • Strong interest to learn fundamentals of Machine Learning and Deep Learning
  • Good academic performance at the end of second year in UG engineering
  • Keen programming interest in Python in Colab environment
  • Good appreciation of applications of Linear Algebra, Probability and Statistics

Regular Minor Program sessions start on 19 Aug 2023.


Grading Policy

  1. 25% weightage for assignments (GitHub)
  2. 75% weightage for examinations (online)
  3. Grades
    Less than 40% – No certificate
    40 – 59% – Completion Certificate
    60 – 74% – B Grade
    75 – 89% – A Grade
    90% above – Outstanding

Module-Wise Examination Date and Time:

Module Number Module Name Examination Date Time
1Representation and Learning29.09.20238.30 pm to 9.30 pm
2Appreciating, Interpreting and Visualizing Data27.10.20238.30 pm to 9.30 pm
3Classification 1: Nearest neighbour method24.11.20238.30 pm to 9.30 pm
4Perceptron and Gradient Descent22.12.20238.30 pm to 9.30 pm
5Classification 2: Powerful popular classifiers19.01.20248.30 pm to 9.30 pm
6Regression and Regularization09.02.20248.30 pm to 9.30 pm
7Unsupervised Learning07.03.20248.30 pm to 9.30 pm
8Probabilistic Perspective05.04.20248.30 pm to 9.30 pm
9MLP and CNN10.05.20248.30 pm to 9.30 pm

Registered Students Details with Exam Score:

Grade Certificate

Certificates Issued for Participants after Completing Semester 1:

Certificates Issued for Participants after Completing Semester 2:

Certificates Issued for Participants after Completing Semester 3:

Feedback From Participants After First Semester:

Program Coordinator:

Teaching Assistant: