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Aditya Guntuboyina (Instructor)

Dohyeong Ki (GSI)

Syllabus

Basic information about the course can be found in the syllabus.

Schedule

Week 1
Jan 21Lecture 1Slides--Introduction and Overview
Jan 23Lecture 2Notes--Simple Linear Regression (Frequentist Inference)
Jan 24Lab part 1Notebook--Fitting Trends via Linear Regression
Dataset 1US Population (from FRED)
Dataset 2USA Accidental Deaths (inbuilt dataset in R)
Lab part 2Notes--Normal Mean Inference
Week 2
Jan 28Lecture 3 NotesNotes--Simple Linear Regression (Bayesian Inference)
Lecture 3 CodeNotebook--More on fitting trends via linear regression
Jan 30Lecture 4 NotesNotes--Bayesian inference in linear regression
Lecture 4 CodeNotebook--Uncertainty Quantification in Linear Regression
Jan 31Lab 2Notebook--Regression Details
Week 3
Feb 4Lecture 5 NotesNotes--Nonlinear Regression (Sinusoidal Model)
Lecture 5 CodeNotebook--Sinusoidal Model Fitting
Dataset 1Annual Sunspots (from https://www.sidc.be/SILSO/datafiles)
Feb 06Lecture 6 NotesNotes--Sum of Squares and Periodogram
Lecture 6 CodeNotebook--Sum of Squares and Periodogram
Dataset 1Audio Middle C file
Feb 07Lab 3Notebook--More on fitting sinusoidal models
Week 4
Feb 11Lecture 7 NotesNotes--Discrete Fourier Transform
Lecture 7 CodeNotebook--Discrete Fourier Transform
Feb 13Lecture 8 NotesNotes--DFT, Periodogram, Nonlinear Regression
Lecture 8 CodeNotebook--Uses of the Periodogram
Feb 14Lab 4Notebook--More on Nonlinear Models
Week 5
Feb 18Lecture 9 NotesNotes--Change of Slope Models
Lecture 9 CodeNotebook--Change of Slope (or Broken-Stick Regression Model)
Feb 20Lecture 10 NotesNotes--High-dimensional linear regression
Lecture 10 CodeNotebook--High dimensional regression -- Ridge and LASSO
Feb 21Lab 5Notebook--Change-point model
Week 6
Feb 25Lecture 11 NotesNotes--Smooth Trend Estimation
Lecture 11 CodeNotebook--Smooth Trend Estimation
Feb 27Lecture 12 NotesNotes--Bayesian regularization
Lecture 12 CodeNotebook--Bayesian regularization
Feb 28Lab 6Notebook--High-dimensional regression for change-points
Week 7
Mar 04Lecture 13 NotesNotes--Sunspots and the Spectrum Model
Lecture 13 CodeNotebook--Sunspots and the Spectrum Model
Mar 04Lecture 14 NotesNotes--Three high-dimensional models
Lecture 14 CodeNotebook--Three high-dimensional models
Mar 07Lab 7Notebook--Spectrum Model applied to a FRED dataset
Week 8
Mar 11Lecture 15 NotesNotes--Spectrum Model
Lecture 15 CodeNotebook--Spectral Analysis
Mar 13Lecture 16 NotesNotes--AutoRegressive Models
Lecture 16 CodeNotebook--AutoRegressive Models
Mar 14Lab 8No notes or code--Review for Midterm
Week 9
Mar 18Lecture Midterm
Mar 20Lecture 17 NotesNotes--Estimation and Predictions for AR models
Lecture 17 CodeNotebook--AR Models (estimation and prediction)
Mar 21Lab 9Notebook--Sunspots Prediction
Week 10
Apr 01Lecture 18 NotesNotes--Estimation, Inference and Prediction for AR models
Lecture 18 CodeNotebook--Estimation, Inference and Prediction for AR models
Apr 03Lecture 19 NotesNotes--Prediction Uncertainty Quantification in AR models
Lecture 19 CodeNotebook--Prediction Uncertainty Quantification in AR models
Apr 04Lab 10Notebook--AR models (estimation, inference and prediction)
Week 11
Apr 08Lecture 20 NotesNotes--Stationarity, MA models, ACF, PACF
Lecture 20 CodeNotebook--Moving Average Models, ACF and PACF
Dataset 1varve dataset (from the Shumway-Stoffer book)
Apr 10Lecture 21 NotesNotes--ACF, PACF, AR models and Stationarity
Lecture 21 CodeNotebook--ACF, PACF, AR models and Stationarity
Apr 11Lab 11Notebook--ACF and PACF
Week 12
Apr 15Lecture 22 NotesNotes--Stationarity of AR models, Box-Jenkins Modeling
Lecture 22 CodeNotebook--Box-Jenkins Modeling
Apr 17Lecture 23 NotesNotes--ARIMA and SARIMA models
Lecture 23 CodeNotebook--ARIMA and SARIMA models
Dataset 1co2 dataset (from the Cryer-Chan book)
Apr 18Lab 12Notebook--Stationary Solutions, Box-Jenkins Modeling, AIC and BIC
Week 13
Apr 22Lecture Notes--No lecture (Professor sick)
Apr 24Lecture 24 NotesNotes--Recurrent Neural Networks
Lecture 24 CodeNotebook--Model Fitting using PyTorch
Apr 25Lab 13Notebook--Model Fitting using PyTorch
Week 14
Apr 29Lecture 25 NotesNotes--Nonlinear AR, RNN, GRU, LSTM
Lecture 25 CodeNotebook--Nonlinear AutoRegression Modeling
May 01Lecture 26 NotesNotes--RNN, GRU, LSTM
Lecture 26 CodeNotebook--LSTM (and RNN, GRU) fitting
May 02Lab 14Notebook--LSTM Neural Network Models
Stat 153
Fitting trends via linear regression