Foundations of Modern Machine Learning – August 2023
The course is designed exclusively for the benefit of second or third year undergraduate engineering students doing four year B Tech program in India. Course starts from August 2023.
This 50-week program is for those who want to unlock the doors to limitless opportunities with our comprehensive learning approach in machine learning. The participants would gain in-demand skills from the comfort of their own home, at their own pace, and are expected to become a sought-after professional in the booming field of artificial intelligence. It would also help accelerate their career in the realm of machine learning through this immersive, live and interactive program administered by faculty members at IIIT Hyderabad. For those who are interested to dive deep into hands-on projects, collaborate with expert assistants, and build a strong foundation in modern machine learning, please read on.
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?
- 50-week certificate program in Foundations of Modern Machine Learning
- Live online lectures and hands-on sessions with personalised learning experience
- Includes over 40 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
- Keen programming interest in Python in Colab environment
- Want to learn applications of Linear Algebra, Probability and Statistics
Frequently Asked Questions (FAQs)
How is this course different from other courses?
This program uniquely combines the benefits of an in-class program with the flexibility of online learning. Recorded classes give the participants the flexibility of learning at their pace. Live interactions with the faculty and mentors help them to clarify their doubts and queries.
Will I receive a certificate at the end of the course?
Yes, a certificate of competency with grades would be awarded upon successful completion of the course.
How will my doubts/queries be resolved in the online class?
Live sessions with the IIIT-H faculty would enable the participants to clear their doubts. Additionally, mentors would be available to clear doubts during the one to one mentoring sessions. Mentors and Project Associates would be available to help the participant with better solutions and workarounds.
Who will be teaching the course?
Live and interactive sessions would be provided by the IIIT-H faculty in conjunction with mentors with considerable AI/ML expertise. Industry experts will also contribute to the learning outcomes through occasional sessions.
What is the expected weekly time commitment?
Participants are expected to commit at least two hours a week to fully benefit from the program. This will include the online sessions and time devoted for learning and assignments as well.
How will I be evaluated during the course?
A holistic approach would be followed where participants would be evaluated continuously. Quizzes, assignments, discussions and attendance would be used for evaluation of performance.
How will I get access to online labs?
All participants would get access to the online labs right from the start of the program.
Does the course have a deferral policy?
When will the live classes be conducted?
The live interactive sessions would usually be conducted on weekends and interactive tutorials would be extended outside working hours of day.
What if I miss a live class?
It is advisable not to miss the live sessions with the faculty. However, in the event of missing a class, recording of the session would be made available for a limited period.
What are the system/internet requirements needed to attend the course?
A laptop/desktop and a stable internet connection is essential to attend the course.
Will I be able to access the learning contents even after completing the course?
The laboratory contents would be built independently by all participants, as part of attending the course, which would be a key takeaway from the one year program. Recorded sessions of classes are set to expire automatically after a fixed duration.
Does IIIT Hyderabad offer a course on Modern Machine Language on-campus ?
No, there is no equivalent on-campus program. IIIT Hyderabad has curated this course, exclusively as an online program.
What is the language of instruction for these courses? Are they available in other regional languages?
All our program courses are taught in English. Hence, a minimum proficiency in English language is expected to participate in the program.
Are there any communication groups on WhatsApp, Telegram etc for the online program on Modern Machine Learning?
Individual email addresses from IIIT domain would be extended to all participants. Discord would be another source of communicating.
What should be done if there is an error with registration?
Please send us your registered email-id, application number and a screenshot of the error/issue with relevant description to email@example.com
Is there an attendance policy for this program?
Yes. Participants are expected to have minimum 75% attendance.
Can we have any hands-on training part (practical lab sessions) ?
All laboratory sessions would be on cloud-based platforms, which would be carried out in week days at a convenient time of participants.
Who will be issuing the certificate ?
IIIT Hyderabad will be issuing the certificate of competency after completion of the course.
Are there any projects to work after the course ?
Those who complete the course with a good rating might be considered for (a) summer internships (with stipend) (b) admission for MS program or (c) working on research projects at IIIT Hyderabad.
Any internship/placement support ?
Participants who perform reasonably well would be extended opportunity to participate in long-term internship programs (with stipend) organised at IIIT Hyderabad
I am a working-professional. Can I join this course ?
This course is exclusively meant for undergraduate engineering students. For working professionals, the appropriate course is https://iiit-h.talentsprint.com/aiml/index.html
I am a teacher in a technical institute. Can I join this course ?
This course is exclusively meant for undergraduate engineering students. For members of faculty, the appropriate course is https://csedu.iiitd.ac.in/program.html There could also be similarly named courses at Coursera or SWAYAM/NPTEL offered in fully online mode.
I have done an NPTEL course on Foundations on Machine Learning. Is this course the same ?
No. This program cannot be compared to any MOOC course. However, SWAYAM/NPTEL course on Introduction to Machine Learning is a good per-requisite for this program.
What is the general opinion about this Course ?
A few UG students who completed the course were contacted for their opinions about the course. Please hear them speaking their mind out.
Important Dates to remember
- Last date of Registration : 31 July 2023 (at iHub-Data)
- Last date of Payment : 31 July 2023 (by participants)
- Regular FMML Sessions from : 19 Aug 2023
- Introduction to ML
- Machine Learning Components: Data, Model, Evaluation
- Revisiting Nearest Neighbor Classification
- Retrieval, Performance Evaluation and Metrics
- Decision Trees
- Linear Classifier
- Representing Textual Data, Aadhar: Sequences matching
- Perceptrons and gradient descent
- Loss functions and gradient descent
- Feature selection and PCA
- Multi Layer Perceptron
- Probabilistic ML models
- Deep Learning Architectures
- 25% weightage for assignments (github)
- 75% weightage for examinations (online)
Less than 40% – No certificate
40 – 59% – Completion Certificate
60 – 74% – B Grade
75 – 89% – A Grade
90% above – Outstanding
For any clarification,
Ph: +91 40 6653 1789 (Mon-Fri 0930h to 1730h)
Module-Wise Examination Date and Time:
|Module Number||Module Name||Examination Date||Time|
|1||Representation and Learning||29.09.2023||8.30 pm to 9.30 pm|
|2||Appreciating, Interpreting and Visualizing Data||27.10.2023||8.30 pm to 9.30 pm|
|3||Classification 1: Nearest neighbour method||24.11.2023||8.30 pm to 9.30 pm|
|4||Perceptron and Gradient Descent||22.12.2023||8.30 pm to 9.30 pm|
|5||Classification 2: Powerful popular classifiers||19.01.2024||8.30 pm to 9.30 pm|
|6||Regression and Regularization||09.02.2024||8.30 pm to 9.30 pm|
|7||Unsupervised Learning||07.03.2024||8.30 pm to 9.30 pm|
|8||Probabilistic Perspective||05.04.2024||8.30 pm to 9.30 pm|
|9||MLP and CNN||03.05.2024||8.30 pm to 9.30 pm|
Registered Students Details with Exam Score:
Academic Progress of FMML Students Following the Completion of the First Three Modules
Feedback from participants after First Quarter