Aditya Guntuboyina (Instructor)
Dohyeong Ki (GSI)
Office Hours (Evans 428):
Monday 5-6 pm
Tuesday 2:30 - 3:30 pm
Thursday 2:30 - 3:30 pm
Shana Soohyun Kim (GSI)
Syllabus¶
Basic information about the course can be found in the syllabus.
Schedule¶
| Aug 28 | Lecture 1 | Slides--Introduction and Overview |
| Sept 02 | Lecture 2 Notes | Notes--Simple Linear Regression | |
| Lecture 2 Code | Notebook--Simple Linear Regression for Time Series | ||
| Lecture 2 In-class | Handwritten Notes | ||
| Sept 04 | Lecture 3 Notes | Notes--Inference for Simple Linear Regression | |
| Lecture 3 In-class | Handwritten Notes |
| Sept 08 | Lab 1 | Notes--Fitting simple trends to data using linear regression | |
| Lab 1 Additional Material | Notes--An integration exercise and MLE example | ||
| Sept 09 | Lecture 4 Notes | Notes--Bayesian Inference for Linear Regression | |
| Lecture 4 Code | Notebook--Linear Regression and Uncertainty Quantification | ||
| Lecture 4 In-class | Handwritten Notes | ||
| Sept 11 | Lecture 5 Notes | Notes--More on posterior in linear regression, and Nonlinear Regression | |
| Lecture 5 Code | Notebook--Standard errors and intervals in linear regression | ||
| Lecture 5 In-class | Handwritten Notes |
| Sept 15 | Lab 2 | Notes--Linear regression details | |
| Sept 16 | Lecture 6 Notes | Notes-- NonLinear Regression | |
| Lecture 6 Code | Notebook--Nonlinear Regression | ||
| Lecture 6 In-class | Handwritten Notes | ||
| Sept 18 | Lecture 7 Notes | Notes--Sinusoidal Model | |
| Lecture 7 Code | Notebook--Sinusoidal Model (applied to the Sunspots Dataset) | ||
| Lecture 7 In-class | Handwritten Notes |
| Sept 22 | Lab 3 | Notes--Inference in Sinusoid Models | |
| Sept 23 | Lecture 8 Notes | Notes--DFT and Periodogram | |
| Lecture 8 Code | Notebook--DFT and Periodogram | ||
| Lecture 8 In-class | Handwritten Notes | ||
| Dataset 1 | Audio Middle C file | ||
| Sept 25 | Lecture 9 Notes | Notes--More on Sinusoidal Models | |
| Lecture 9 Code | Notebook--More on Sinusoidal Models | ||
| Lecture 9 In-class | Handwritten Notes |
| Sept 29 | Lab 4 | Notes--Change-point Model | |
| Sept 30 | Lecture 10 Notes | Notes--High-dimensional Linear Regression | |
| Lecture 10 Code | Notebook--Change of Slope Models and Regularization | ||
| Lecture 10 In-class | Handwritten Notes | ||
| Oct 02 | Lecture 11 Notes | Notes--Ridge and LASSO regularization | |
| Lecture 11 Code | Notebook--Ridge and LASSO Regularization | ||
| Lecture 11 In-class | Handwritten Notes |
| Oct 06 | Lab 5 | Notes--High-dimensional regression for change-points | |
| Oct 07 | Lecture 12 Notes | Notes--Bayesian Regularization | |
| Lecture 12 Code | Notebook--Bayesian Regularization | ||
| Lecture 12 In-class | Handwritten Notes | ||
| Oct 09 | Lecture 13 Notes | Notes--More on Bayesian Regularization | |
| Lecture 13 Code | Notebook--More on Bayesian Regularization | ||
| Lecture 13 In-class | Handwritten Notes |
| Oct 13 | Lab 6 | Notebook-Bayesian Regularization | |
| Oct 14 | Lecture 14 Notes | Notes--Three High-dimensional Models | |
| Lecture 14 Code | Notebook--Three High-Dimensional Models | ||
| Lecture 14 In-class | Handwritten Notes | ||
| Oct 16 | Lecture 15 Notes | Notes--Spectrum Model | |
| Lecture 15 Code | Notebook--Spectrum Model | ||
| Lecture 15 In-class | Handwritten Notes |
| Oct 20 | Lab 7 | Midterm review | |
| Oct 21 | Lecture | Midterm | |
| Oct 23 | Lecture 16 Notes | Notes--AutoRegressive Models | |
| Lecture 16 Code | Notebook--AutoRegressive Models | ||
| Lecture 16 In-class | Handwritten Notes |
| Oct 27 | Lab 8 | Notebook-Sunspots Prediction | |
| Oct 28 | Lecture 17 Notes | Notes--Estimation for AR(1) | |
| Lecture 17 Code | Notebook--Estimation for AR(1) | ||
| Lecture 17 In-class | Handwritten Notes | ||
| Oct 30 | Lecture 18 Notes | Notes--Estimation and Prediction for AR(p) models | |
| Lecture 18 Code | Notebook--Estimation and Prediction for AR(p) models | ||
| Lecture 18 In-class | Handwritten Notes |
| Nov 03 | Lab 9 | Notebook-AR models: estimation, inference and prediction | |
| Nov 04 | Lecture 19 Notes | Notes--Stationarity | |
| Lecture 19 In-class | Handwritten Notes | ||
| Nov 06 | Lecture 20 Notes | Notes--Stationarity of AR models | |
| Lecture 20 Code | Notebook--AR(p) models and stationarity | ||
| Lecture 20 In-class | Handwritten Notes |
| Nov 10 | Lab 10 | Notebook-Causal Stationary AR Models | |
| Nov 13 | Lecture 21 Notes | Notes--MA(q), Sample ACF and the Box-Jenkins Approach | |
| Lecture 21 Code | Notebook--MA models and ARIMA modeling | ||
| Lecture 21 In-class | Handwritten Notes |
| Nov 17 | Lab 11 | Notebook-AR Model Fitting: AutoReg vs ARIMA | |
| Nov 18 | Lecture 22 Notes | Notes--ARMA and ARIMA models | |
| Lecture 22 Code | Notebook--ARIMA modeling | ||
| Lecture 22 In-class | Handwritten Notes | ||
| Nov 20 | Lecture 23 Notes | Notes--SARIMA models | |
| Lecture 23 Code | Notebook--SARIMA modeling | ||
| Lecture 23 In-class | Handwritten Notes |
| Nov 24 | Lab 12 | Notebook-MA(1) parameter estimation | |
| Nov 25 | Lecture 24 Notes | Notes--Nonlinear AutoRegression | |
| Lecture 24 Code | Notebook--PyTorch Model Fitting and Nonlinear AutoRegression | ||
| Lecture 24 In-class | Handwritten Notes |
| Dec 01 | Lab 13 | Notebook--More on Model Fitting using PyTorch | |
| Dec 02 | Lecture 25 Notes | Notes--Recurrent Neural Networks | |
| Lecture 25 In-class | Handwritten Notes | ||
| Dec 04 | Lecture 26 Notes | Notes--AR to LSTM | |
| Lecture 26 Code | Notebook--LSTM | ||
| Lecture 26 In-class | Handwritten Notes |