Machine Learning 2026 Course Portal

Policies

Course portal

Policy area	Policy
Submission format	Every assignment must be submitted as a single PDF file named <roll_number>_<first_name>_<last_name>.pdf.
Evaluation	Evaluation focuses on correctness, clarity, reasoning, and learning rather than length. Code should be included as an annexure or submitted separately only when explicitly requested.
Quizzes and exams	Quizzes may be surprise or announced. Mid-semester and end-semester exams are closed book and closed notes unless explicitly stated otherwise.
Devices	No phones, laptops, tablets, smart watches, or electronic devices are allowed in class or quizzes unless explicitly requested for a tutorial or coding activity.
Class entry	Class doors close after 5 minutes. There is no attendance credit as such, but no credit or make-up will be given if a surprise quiz happens after a student is late or absent.
Course notebook	Students must maintain a separate course notebook and bring it to every class. Only this notebook will be allowed for open-notebook quizzes or exams, if any are announced.
Use of AI/GenAI tools	AI/GenAI tools are permitted for assignments, but students are accountable for every final answer, equation, plot, claim, and line of code. A viva on any assignment may happen at any time.
PG component	ES 678 students receive additional assessed depth through PG-specific exam questions and a research/depth addendum in the course project.

Component	Weight	Basis
Quizzes	10%	Surprise or announced quizzes. If more than five quizzes are conducted, the best five may be counted at the instructor's discretion.
Assignments A0-A5	30%	A0 is graded and included. All assignment questions are common to ES 335 and ES 678 students.
Mid-semester examination	20%	Closed book and closed notes unless explicitly announced otherwise.
End-semester examination	25%	Closed book and closed notes unless explicitly announced otherwise. PG students may receive additional assessed depth through PG-specific exam questions.
Course project	15%	End-to-end ML study with proposal, final report, reproducible analysis, and PG research/depth addendum where applicable.