50-week Foundations of Modern Machine Learning

Introduction

Machine Learning has diffused into almost all walks of our life and promises to engage us more intensively in years to come. Knowledge of machine learning will soon be an imperative, if one is determined to engage technology as a profession. Technology innovation hub of IIIT Hyderabad (iHub-Data) has strong research programs in machine learning, image processing, computer vision, robotics, natural language processing, pattern recognition and speech processing, and is pleased to announce a 50-week foundation program in machine learning for aspiring engineering students across India, slated to commence on 03 October 2021.

Latest Update:

Who can participate?

• Students pursuing 4-year UG program in CSE/IT/EEE/ECE or any allied streams
• Students should be Indian residents

What makes this program unique?

• 50-week certificate program in Foundations of Modern Machine Learning
• Equivalent to a typical 4 credit course as per UGC/AICTE norms
• Live online lectures and hands-on sessions with personalised learning experience
• Equal focus on foundation and practices
• Discussions with eminent professionals

What is the qualifying criteria?

• Strong interest to learn Machine Learning fundamentals
• Keen programming interest in Python/Colab
• Have a basic understanding of Linear Algebra, Probability and Statistics

Important Dates to remember

• Last Date of Registration : 14 September 2021
• Preparatory Module (from) : 15 September 2021
• Regular Sessions (from) : 03 October 2021

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.
• Are fellowships/scholarships available?
  A few scholarships are available for deserving meritorious students.
• Will I receive a certificate at the end of the course?
  Yes, a certificate of achievement from IIIT-H will 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 will enable the participants to clear their doubts. Additionally, mentors will be available to clear doubts during the one to one mentoring sessions. Mentors and Project Associates are there to help the participant with better solutions and workarounds.
• Who will be teaching the course?
  Recorded and live sessions will 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 3 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?
  No
• When will the live classes be conducted?
  The live interactive sessions will usually be conducted on weekends or 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 is what all participants would build independently as part of attending the course. 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 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 emails 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 fmml.coordinator@ihub-data.iiit.ac.in .If the error is after payment has been made, please forward the confirmation email along with email-id, application number and screenshot with description.
• 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.
• Who will be issuing the certificate ?
  IIIT Hyderabad will be issuing the certificate of 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 (b) pursuing higher studies 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  summer internship programs organised at IIIT Hyderabad

Curriculum

• Introduction to ML
• Machine Learning Problems
• Learning and Data
• Data Transformations
• Data Visualization and Plotting
• Principal Component Analysis
• Non-Linear Dimensionality Reduction
• Advances in DR for Visualization
• Re-visiting NN Classification
• Performance Evaluation Metrics
• Representing Textual Data
• String Matching and Applications
• Perceptrons, Gradient Descent
• Gradient Descent – A deeper dive
• Margin and Multi-class Classifiers
• Linear SVM
• Non-linear SVM
• Decision Trees
• Decision Tree Learning
• Random Forests and Ensembles
• Linear regression, MSE and polynomial regression

Grading Policy

25% weightage for assignments (github)
75% weightage for examinations (online)
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Grades

Less than 40% – No certificate
40 – 49% – Completion Certificate (Grade P)

50 – 59% – Grade C
60 – 74% – Grade B
75 – 89% – Grade A
90% above – Outstanding (Grade O)

Final Grades for Participants

Program Coordinators:

Teaching Assisstants: