Aditya Guntuboyina (Instructor)
Aidan McLoughlin (GSI)
Yichen Pan (GSI)
Syllabus¶
Basic information about the course can be found in the syllabus.
Schedule¶
| Aug 27 | Lecture 1 | Slides--Introduction and Overview |
| Sept 01 | Lab 1 | Notes--Linear Regression for Time Series | |
| Sept 01 | Lecture 2 Notes | Notes--Linear Regression for Time Series | |
| Lecture 2 Code | Notebook--Linear Regression Models fit to the US population data | ||
| Dataset 1 | US Census Bureau 2023 Population Projections | ||
| Dataset 2 | UN 2024 Population Projections | ||
| Sept 03 | Lecture 3 Notes | Notes--Simple Bayesian Inference Examples | |
| Lecture 3 In-class | Handwritten Notes |
| Sept 08 | Lab 2 | Notes--Linear Regression Details | |
| Dataset 1 | GDP data from FRED | ||
| Sept 08 | Lecture 4 Notes | Notes--Multiple Linear Regression | |
| Lecture 4 Code | Notebook--Multiple Linear Regression | ||
| Dataset 1 | US Accidental Deaths | ||
| Dataset 2 | FRED retail alcohol sales data | ||
| Lecture 4 In-class | Handwritten Notes | ||
| Sept 10 | Lecture 5 Notes | Notes--Bayesian Inference for Multiple Linear Regression | |
| Lecture 5 Code | Notebook--Linear Regression and Uncertainty Quantification | ||
| Lecture 5 In-class | Handwritten Notes |
| Sept 15 | Lab 3 | Notes--From linear regression to Nonlinear regression | |
| Dataset 1 | US Corn Yield data | ||
| Dataset 2 | Weight Loss data | ||
| Sept 15 | Lecture 6 Notes | Notes--NonLinear Regression | |
| Lecture 6 Code | Notebook--NonLinear Regression | ||
| Dataset 1 | CA population data from FRED | ||
| Lecture 6 In-class | Handwritten Notes | ||
| Sept 17 | Lecture 7 Notes | Notes--Sinusoidal Model | |
| Lecture 7 Code | Notebook--Sinusoidal Model | ||
| Dataset 1 | Annual Sunspots Data | ||
| Dataset 2 | Audio Middle C file | ||
| Lecture 7 In-class | Handwritten Notes |
| Sept 22 | Lab 4 | Notes--Inference in sinusoid models | |
| Sept 22 | Lecture 8 Notes | Notes--DFT, Periodogram and RSS | |
| Lecture 8 Code | Notebook--DFT, Periodogram and RSS | ||
| Lecture 8 In-class | Handwritten Notes | ||
| Sept 24 | Lecture 9 Notes | Notes--Models with multiple sinusoids | |
| Lecture 9 Code | Notebook--More on sinusoidal models | ||
| Lecture 9 In-class | Handwritten Notes |
| Sept 29 | Lab 5 | Notes--Change-point Models | |
| Sept 29 | Lecture 10 Notes | Notes--Change of slope models with more parameters | |
| Lecture 10 Code | Notebook--Change of slope models with more parameters | ||
| Lecture 10 In-class | Handwritten Notes | ||
| Oct 01 | Lecture 11 Notes | Notes--High-dimensional linear regression with ridge and LASSO regularization | |
| Lecture 11 Code | Notebook--High-dimensional linear regression with ridge and LASSO regularization | ||
| Lecture 11 In-class | Handwritten Notes | ||
| Dataset 1 | CA average monthly temperatures | ||
| Dataset 2 | AL average monthly temperatures | ||
| Dataset 3 | CA maximum daily temperatures | ||
| Dataset 4 | CA minimum daily temperatures |
| Oct 06 | Lab 6 | Notes--High-dimensional regression with change points | |
| Oct 06 | Lecture 12 Notes | Notes--Ridge regularization from the Bayesian perspective | |
| Lecture 12 Code | Notebook--Ridge regularization from the Bayesian perspective | ||
| Lecture 12 In-class | Handwritten Notes | ||
| Oct 08 | Lecture 13 Notes | Notes--Bayesian Ridge regularization | |
| Lecture 13 Code | Notebook--Bayesian Ridge regularization | ||
| Lecture 13 In-class | Handwritten Notes |
| Oct 13 | Lab 7 | Notes--Cross-Validation vs Bayesian Regularization for Ridge Regression | |
| Oct 13 | Lecture 14 Notes | Notes--Spectrum Model | |
| Lecture 14 Code | Notebook--Variance Models and the Spectrum Model | ||
| Lecture 14 In-class | Handwritten Notes | ||
| Oct 15 | Lecture 15 Notes | Notes--Smoothing the periodogram | |
| Lecture 15 Code | Notebook--Smoothing the periodogram | ||
| Lecture 15 In-class | Handwritten Notes |
| Oct 20 | Lab 8 | Midterm review | |
| Oct 20 | Lecture 16 Notes | Notes--AutoRegressive Models | |
| Lecture 16 Code | Notebook--AutoRegressive Models | ||
| Lecture 16 In-class | Handwritten Notes | ||
| Oct 22 | Lecture | Midterm |
| Oct 27 | Lab 9 | Notes--Sunspots Prediction | |
| Oct 27 | Lecture 17 Notes | Notes--Parameter Estimation in AutoRegressive Models | |
| Lecture 17 Code | Notebook--Parameter Estimation in AutoRegressive Models | ||
| Lecture 17 In-class | Handwritten Notes | ||
| Oct 29 | Lecture 18 Notes | Notes--Predictions from AR(p) models, and AutoCorrelations | |
| Lecture 18 Code | Notebook--AR(p) modeling, predictions, and autocorrelations | ||
| Lecture 18 In-class | Handwritten Notes |
| Nov 02 | Lab 10 | Notes--AR models (estimation, inference and prediction) | |
| Nov 03 | Lecture 19 Notes | Notes--AR predictions, stability and MA models | |
| Lecture 19 Code | Notebook--Prediction and Order Selection in AR Models | ||
| Lecture 19 In-class | Handwritten Notes | ||
| Nov 05 | Lecture 20 Notes | Notes--Parameter estimation in MA models | |
| Lecture 20 Code | Notebook--MA Models | ||
| Lecture 20 In-class | Handwritten Notes |