Lecture Schedule : Tuesday and Friday 2:00 to 3:25 pm.
Venue : SIC 301, Rekhi building.
Instructor's office hours check here
Prerequisites An
upper-level undergraduate course(s) in algorithms and data structures,
a basic course on probability and statistics. This is a first course
on data mining and no prior knowledge of data mining or machine
learning is assumed. Homework assignments will require programming in
Java, which can sometimes be substituted with C++
Post-requisites
This or CS705 is prerequisite for the course "Graphical models and
structured learning" taught in the spring semester.
Eligibility Requirement
The course is open to CS MTechs, PhD, DD and BTech students.
Students of other departments
should approach for permission only if they meet the necessary pre-requisites.
Credit/Audit Requirements
Approximate credit structure
| 20% |
Midsem exam |
| 40% |
Endsem exam |
| 25% |
Homeworks/Scribes |
| 15% |
Three short surprise quizzes
(best two of three quizzes used for grading. All quizzes will be surprise and there will be no compensation for missed quizzes except under very special circumstances.)
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Grading for audit students:
Audit students have to score more than 30% over all, including assignments, quizzes and mid/end sem exam.
Mail to:
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