Course Notes
Detailed Learning Outcomes
Unit 1 – Probability Theory and Stochastic Processes
- Lecture 1 – Probability Theory
- Supplement – Taking Fourier Transforms (Mathematica notebook)
- Supplement – Probabililty Distributions (Jupyter notebook)
- Supplement – Examples of Probabililty Distributions (Zip folder with images)
- Supplement – 2D Normal Distribution (Jupyter notebook)
- Stochastic Processes (Postponed until next year)
Unit 2 – Mechanics and Phase Space
- Lecture 2 – Mechanics and Phase Space
- Supplement – Classical Mechanics Examples Zipped Folder with code, images, Mathematica notebooks, and video
- Story of your life
- Supplement – Quantum Mechanics Examples Zipped Folder with code, images, Mathematica notebooks, and video
- Feynman lectures on QM
- 3Blue1Brown on the uncertainty principle
- 3Blue1Brown on the physics of light and QM
- Image of traveling and standing waves from Wikipedia
- Double slit experiment from Wikipedia
Unit 3 – Ensemble Theory
- Lecture 3 – Ensemble Theory
- Supplement – Math for Binomial Coefficient for Large M (Mathematica notebook)
- Supplement – Binomial Coefficient for Large M (Jupyter notebook)
- Supplement – Gibbs Entropy Formula for a Gaussian (Jupyter notebook)
- Supplement – Ideal Gas Entropy (Jupyter notebook)
Unit 4 – Pure Component Phase Behavior
- Lecture 4 – Pure Component Phase Behavior
- Supplement – van der Waals fluid (Jupyter notebook)
- Liquids and solids coming next year
Lecture 5 – Soft Materials: polymers, colloids, electrolytes
- Polymer Physics Notes from Spring 2023: Link
Lecture 6 – Non-equilibrium: transport and stochastic kinetics
- Next year