APPLIED PROBABILITY PPT AND VIDEO LECTURES

APPLIED PROBABILITY PPT AND VIDEO LECTURES


Instructor: Tina Kapur and Rajeev Surati

Course Description 

Focuses on modeling, quantification, and analysis of uncertainty by teaching random variables, simple random processes and their probability distributions, Markov processes, limit theorems, elements of statistical inference, and decision making under uncertainty. This course extends the discrete probability learned in the discrete math class. It focuses on actual applications, and places little emphasis on proofs. A problem set based on identifying tumors using MRI (Magnetic Resonance Imaging) is done using Matlab.

Text:   Fundamentals of Applied Probability Theory, Al Drake

Lecture Notes

lecture1.ppt
lecture2.ppt
lecture3.ppt
lecture4.ppt
Lecture_05.pdf
Lecture_05.ppt

Lecture Videos

07-02-01: Introduction, Algebra of Events, Conditional Probability
07-03-01: Independence, Bayes Theorem, Probability Mass Functions
07-05-01: Conditional PMFs, Probability Density Functions
07-06-01: PDFs and Image Guided Surgery
07-09-01: Bayesian Segmentation of MRI Images

Problem Sets

ground_truth_test_img
MRI.tar
MRI_Link
mri_read.m
mri_test_img
Problem_Set_04.txt
Problem_Set_05.txt
pset01.txt
pset04.txt
SEG.tar
SEG_Link