**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.pptlecture2.ppt

lecture3.ppt

lecture4.ppt

Lecture_05.pdf

Lecture_05.ppt

### Lecture Videos

07-02-01: Introduction, Algebra of Events, Conditional Probability07-03-01: 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_imgMRI.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

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