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TAM 598 - Engineering and Scientific Applications of Machine Learning

Introduction
- Lecture 1 - Introduction to Predictive Modeling
Review of Probability
Uncertainty Propagation
Principles of Bayesian Inference
- Lecture 11 - Selecting Prior Information
- Lecture 12 - Analytical Examples of Bayesian Inference
Supervised Learning
Unsupervised Learning
- Lecture 17 - Clustering and Density Estimation
- Lecture 18 - Dimensionality Reduction
State Space Models
- Lecture 19 - State Space Models - Filtering Basics
  - State Space Models - Filtering Basics
  - Object Tracking Example
- Lecture 20 - State Space Models - Kalman Filters
  - State Space Models - Kalman Filters
  - Kalman Filter for the Object Tracking Example
Gaussian Process Regression
Neural Networks
Advanced Methods for Characterizing Posteriors
- Lecture 27 - Sampling Methods
- Lecture 28 - Variational Inference
  - Variational Inference
  - Variational Inference Examples
Homework
Bibliography

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By Ilias Bilionis (ibilion[at]purdue.edu)