Aditya Guntuboyina (Instructor)
Reece Huff (GSI)
Syllabus¶
Basic information about the course can be found in the syllabus.
Topics¶
Here is a tentative list of topics.
Schedule¶
| Jan 21 | Lecture 1 | Introduction | |
| PDF 1 | LectureOne238Spring2026 | ||
| Jan 23 | Lecture 2 | Simple Examples | |
| PDF 2 | LectureTwo238Spring2026 |
| Jan 26 | Lecture 3 | Normal Mean Example | |
| PDF 3 | LectureThree238Spring2026 | ||
| Jan 27 | Lab 1 | Bayes Rule, LOTP, and Model Selection | |
| PDF Lab 1 | lab01.pdf | ||
| Jan 28 | Lecture 4 | Likelihood Principle | |
| PDF 4 | LectureFour238Spring2026 | ||
| Jan 30 | Lecture 5 | Bayesian Testing | |
| PDF 5 | LectureFive238Spring2026 |
| Feb 02 | Lecture 6 | Interpretation and Rules of Probability | |
| PDF 6 | LectureSix238Spring2026 | ||
| Feb 03 | Lab 2 | Uninformative Priors | |
| Lab 2 Code | Frequentist vs. Bayesian Inference | ||
| PDF Lab 2 | lab02.pdf | ||
| Feb 04 | Lecture 7 | Recap of the derivation of the rules of probability | |
| Lecture 7 Code | Kidney Cancer Data Analysis | ||
| PDF 7 | LectureSeven238Spring2026 | ||
| Dataset 1 | Kidney Cancer Dataset | ||
| Feb 06 | Lecture 8 | Beta-Binomial Inference | |
| PDF 8 | LectureEight238Spring2026 |
| Feb 09 | Lecture 9 Code | Kidney Cancer Data Analysis with Bayesian Methods | |
| Dataset 1 | Kidney Cancer Dataset | ||
| Feb 10 | Lab 3 Code | Probabilistic Programming via PyMC | |
| Feb 11 | Lecture 10 | Additional comments on kidney cancer data analysis, and normal likelihoods | |
| Lecture 10 Code | More on Kidney Cancer Data Analysis, and the baseball dataset | ||
| PDF 10 | LectureTen238Spring2026 | ||
| Feb 13 | Lecture 11 | Normal likelihoods, and the James-Stein estimator | |
| Lecture 11 Code | Normal-Normal Bayesian model applied to the baseball dataset | ||
| PDF 11 | LectureEleven238Spring2026 |
| Feb 17 | Lab 4 Code | Posterior Predictive Distribution | |
| Feb 18 | Lecture 12 | Dirichlet-Multinomial Inference | |
| Lecture 12 Code | Dirichlet-Multinomial Inference | ||
| PDF 12 | LectureTwelve238Spring2026 | ||
| Feb 20 | Lecture 13 | Bayesian Bootstrap | |
| Lecture 13 Code | Bayesian Bootstrap | ||
| PDF 13 | LectureThirteen238Spring2026 |
| Feb 23 | Lecture 14 | Simple text analysis example | |
| Lecture 14 Code | Simple text analysis example | ||
| PDF 14 | LectureFourteen238Spring2026 | ||
| Feb 24 | Lab 5 Code | Bayesian Bootstrap | |
| Feb 25 | Lecture 15 | Linear Regression | |
| PDF 15 | LectureFifteen238Spring2026 |
| Mar 02 | Lecture 16 | Linear regression -- frequentist and Bayesian inference | |
| Lecture 16 Code | Linear Regression | ||
| PDF 16 | LectureSixteen238Spring2026 | ||
| Mar 03 | Lab 6 Code | Linear Regression Details | |
| Dataset 2 | GDP Dataset | ||
| Mar 04 | Lecture 17 | Logistic Regression | |
| Lecture 17 Code | Logistic Regression | ||
| PDF 17 | LectureSeventeen238Spring2026 | ||
| Mar 06 | Lecture 18 Code | High-dimensional linear regression |
| Mar 09 | Lecture 19 | High-dimensional linear regression -- Bayesian inference | |
| Lecture 19 Code | High-dimensional linear regression | ||
| PDF 19 | LectureNineteen238Spring2026 | ||
| Mar 10 | Lab 7 Code | Poisson Regression | |
| Dataset 3 | MROZ Dataset | ||
| Mar 11 | Lecture 20 | Bayesian inference for high-dimensional linear regression (continued) | |
| Lecture 20 Code | Bayesian inference for high-dimensional linear regression (continued) | ||
| PDF 20 | LectureTwenty238Spring2026 | ||
| Mar 13 | Lecture 21 | Introduction to Gaussian Process Regression | |
| Lecture 21 Code | Simulating Brownian Motion | ||
| PDF 21 | LectureTwentyOne238Spring2026 |
| Mar 16 | Lecture 22 | Interpolation and Integration using Gaussian Processes | |
| Lecture 22 Code | Interpolation with Gaussian Processes | ||
| PDF 22 | LectureTwentyTwo238Spring2026 | ||
| Mar 17 | Lab 8 Code | Bayesian Regularization | |
| Mar 18 | Lecture 23 | Regression using Gaussian Processes | |
| Lecture 23 Code | Gaussian Process Regression | ||
| PDF 23 | LectureTwentyThree238Spring2026 |
| Mar 30 | Lecture 24 | MCMC: The Metropolis Algorithm | |
| Lecture 24 Code | MCMC via the Metropolis Algorithm | ||
| PDF 24 | LectureTwentyFour238Spring2026 | ||
| Mar 31 | Lab 9 Code | GP Regression with the squared exponential kernel | |
| Apr 01 | Lecture 25 | Markov chains, detailed balance, Metropolis-Hastings and Gibbs | |
| PDF 25 | LectureTwentyFive238Spring2026 | ||
| Apr 03 | Lecture 26 | The Gibbs Sampler | |
| Lecture 26 Code | Gibbs sampler -- simple exampls | ||
| PDF 26 | LectureTwentySix238Spring2026 |