Instructor
Prof. Baskar S
Motivation
Interest in probability, statistics and data science
Prerequisites
None
Course Content and Structure
Axioms of Probability, Conditional Prob-ability and Independence, Random variables and distribution functions, Random vectors and joint distributions, Functions of random vectors.Expectation, moment generating functions and characteristic functions, Conditional expectation and distribution. Modes of convergence, Weak and strong laws of large numbers, Central limit theorem.
Weightage
60% quizzes + 20% midsem + 20% endsem
Feedback on lectures, tutorials and exams
lectures were informative, exams were open book and open internet. mostly questions came from tutorial and lectures. Overall lectures were good and Baskar sir’s teaching style was great. Topics can seem tough at start but in the end everything makes sense.
This course further helps a lot in CL202 another course in chemical department
Attendance
Not mandatory
Difficulty level
4
Grading Stats
AA 23
AB 28
BB 20
BC 12
CC 4
CD 1
DD 1
II 3
Total 92
References
P. Billingsley, Probability and Measure, Anniversary Edition, John Wiley & Sons (SEA) Pvt. Ltd., February 2.P.G. Hoel, S.C. Port and C.J. Stone, Introduction to Probability, Universal Book Stall, New Delhi, 1998.J.S. Rosenthal, A First Look at Rigorous Probability Theory, 2nd Editon World Scientific. 2006.M. Woodroofe, Probability with Applica-tions, McGraw-Hill Kogakusha Ltd., Tokyo, 1975.
We thank Raghav Gupta for this review
Department Academic Mentorship Program 