APPLIED PROBABILITY PPT AND VIDEO LECTURESInstructor: 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.pptlecture3.pptlecture4.pptLecture_05.pdfLecture_05.pptLecture Videos
07-02-01: Introduction, Algebra of Events, Conditional Probability07-03-01: Independence, Bayes Theorem, Probability Mass Functions07-05-01: Conditional PMFs, Probability Density Functions07-06-01: PDFs and Image Guided Surgery07-09-01: Bayesian Segmentation of MRI ImagesProblem Sets
ground_truth_test_imgMRI.tarMRI_Linkmri_read.mmri_test_imgProblem_Set_04.txtProblem_Set_05.txtpset01.txtpset04.txtSEG.tarSEG_Link
