ECE 695E Data Analysis, Design of Experiment, ML Lecture 14: Physics-based Machine Learning

Table of Contents:
00:00 Lecture 14. Physics-based Machine Learning
01:44 Copyright 2018
01:46 Course Outline
01:48 Outline
03:05 Galileo experiments
05:54 Galileo vs. Newton
08:44 Physics-based machine learning approach
10:29 Physics-based machine learning approach
13:22 Outline
13:23 A ball falling under gravity (idealized)
15:54 A falling ball with height dependent gravity (idealized)
17:43 A falling ball with air resistance
19:23 A falling ball with gravity and resistance
21:10 Outline
21:34 Physics-based machine learning approach
24:26 An example of lake temperature: The master variable
30:35 A example involving lake temperature
36:30 Outline
38:19 Structural Equation modeling: Motivation
40:24 Structural Equation modeling: Examples
45:00 Example: Degradation of Solar Cells
47:52 PV Degradation (continued)
48:58 Untitled: Slide 23
49:21 Conclusions
50:03 Review Questions
50:10 References

This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation. We will also discuss principles of design of experiments so that the data generated by experiments/simulation are statistically relevant and useful. We will conclude with a discussion of analytical tools for machine learning and principle component analysis. At the end of the course, a student will be able to use a broad range of tools embedded in MATLAB and Excel to analyze and interpret their data.

Topics Covered:
Review of Basic Statistical Concepts
Where do data come from: Big vs. Small Data
Collecting and Plotting Data: Principles of Robust Data Analysis
Physical vs. Empirical Distribution
Model Selection and Goodness of Fit
Scaling Theory of Design of Experiments
Statistical Theory of Design of Experiments
Machine Learning vs. Physics-based Machine Learning