Machine Learning and Statistical Learning Theory 2

All course materials (homework/labs/tests/etc.) are on Canvas.

• Syllabus:  Math7339Spring2023SyllabusMachineLearning2

Lecture Notes:

Part 1:  Advanced Machine Learning

0. Syllabus and Introduction Math7339Sec0Introduction

1. Bias-variance trade-off  Math7339Sec1Bias-Variance

2. Model Complexity Math7339Sec2Complexity

3. Generalized linear methods. Math7339Sec3GLRegression

4. Splines and generalized additive models Math7339Sec4Spline1 and Math7339Sec4Spline2GAM

5. Smoothing methods Math7339Sec5Smoothing

6. Bayesian methods Math7339Sec6Bayesian

7. Latent variables Math7339Sec7LatentVariables

8. Kernel methods and Reproducing kernel Hilbert space Math7339Sec8Kernel

Part 2: Time Series:

1.Times Series Introduction Math7339TS1TimesSeries_Intro

2. Station Process Math7339TS2StationaryProcess

3. ARMA Math7339TS3ARMA

4. Forecasting Math7339TS4Forecasting

5. Exploratory Data Analysis Math7339TS5Exploratory Data Analysis

6. ARIMA Math7339TS6ARIMA

7. Spectral Analysis Math7339TS7SpectralAnalysis

8. FFT and Wavelet Math7339TS8 FFT_Wavelet

9. Automatic models and Prophet Math7339TS9Prophet

10. Deep learning on time series data Math7339TS10DeepLearning