Proposed Syllabus:
Bayesian Inference and Modeling: Prior and posterior distributions, Bayesian models, Bayesian regression, Hierarchical Bayes models.
Logistic regression; odds ratio, concordance-discordance measures, Logistic Regression as a classifier. Probit Regression. Introduction to Multilogit models. Modeling count data: Poisson Regression, Poisson models for zero inflated data.
Introduction and visualizing categorical data. Measures of association. Loglinear Models, Models for nominal and ordinal response.
Survival Data Modeling: Time-to-event data and survival probabilities, notion of censoring, survival curve and other ways of representing survival distribution, Kaplan-Meier and Nelson-Aalen estimates, log-rank test, Cox's proportional hazard model. Parametric survival models for Exponential, Gamma, Wiebull distributions.
Introduction to mixture models.
Illustration of the methodology with real data.
Teaching Time: 11am - 12:50pm on Wednesdays and 2:30 - 4:20pm on Fridays (to be conducted by Chirayata)
Weightage (to be finalized):
Assignments: 10% (ZERO score will be provided if assignments are submitted late),
Quizzes: 20%,
MidSem exam: 30%,
EndSem exam: 50%.
All tests and exams will be closed book.
Textbook: Foundations of Statistical Science for Data Scientists: With R and Python by Alan Agresti and Maria Kateri
Webpage: https://stat4ds.rwth-aachen.de/
R codes: https://stat4ds.rwth-aachen.de/pdf/DS_R_webAppendix.pdf
Datasets: https://stat4ds.rwth-aachen.de/data/
Guidelines:
● Attend ALL lectures. Attendance will be taken.
● ALL evaluations will be offline, but grading will be done online using Gradescope.
● Study the covered materials after each class and be prepared in the next class.
● Try to develop an intuition on why the methods work.
● Feel free to use Internet search while studying and learning new concepts.
● If you have questions, comments, etc. do not hesitate to ask/email me.
Extra reading material:
1.
2,