**Applied Machine Learning** (Spring 2022)

The second version of the course I developed in fall of 2019 (see full description below) has now been co-listed as an upper division undergraduate course.

**Microscopic Simulation Methods (MSE 991)** (Fall 2021)

This is a modification of the graduate course I taught on particle-based simulation methods – see the full description below. This variant covered only two methods: Monte Carlo methods (Metropolis, SSA, tau-leap) and molecular dynamics. Along the way, related background topics were covered: mechanics, statistical mechanics, thermodynamics, Markov chains and integrators. The course was 50/50 lecture/flipped, with in-class projects each week.

**High Energy-Density Physics** (Spring 2020)

This course is taught by University of Michigan by Prof. Kuranz and shared with MSU as a special topics course. I am the MSU faculty member for the course, but I act primarily as a local organizer and not an instructor. If you are interested in this course, contact me or Prof. Kuranz at University of Michigan.

**Applied Machine Learning** (Fall 2019)

This is a graduate level (CMSE 890) course that is project based with in-class group coding projects every week. A major component of the course is a capstone three-hour poster session of individual projects (typically connected with the PhD topic of the student) by each student.

*Prerequisites*

- Computational modeling (see below)
- Multivariable calculus
- Advanced Python programming
- Some experience with linear algebra, statistics and optimization

*Topics*

- Scientific Python stack (NumPy, SciPy, Pandas, Seaborn, Matplotlib, Scikits)
- Linear Algebra
- Statistics
- Optimization
- Classifiers (decision trees, support vector machines, perceptrons, KNN)
- Gaussian process regression
- Neural networks (neural network zoo)
- Dimensionality reduction (PCA, AE, LLE, MDS)
- Tools of the trade: confusion matrices, pipelines, learning curves, hyperparameter optimization, visualization, cross validation, etc.
- Complete Scikit-Learn ecosystem and introduction to Keras (TF 2.0)
- History of machine learning

*Textbook*

**Kinetic Theory** (Fall 2018)

*Prerequisites*

*Topics*

*Textbook*

**Particle-Based Simulation Methods** (Spring 2018)

This is a graduate-level special topics course. Like most courses I have developed, this is a flipped classroom course with in-class group projects alternating with lectures/discussions. As with all special topics course, the content can be adpated to the needs of the students.

*Prerequisites*

- Basic knowledge of physics (ideally the equivalent to an undergrad in physics)
- Very strong programming skills in any language (many students use high-performance Python)

*Topics*

- Molecular dynamics (MD)
- Dissipative particle dynamics (DPD)
- Particle in cell (PIC)
- Smoothed-particle hydrodynamics (SPH)

*Textbook*

There is no textbook for this course, but I provide notes that are in the form of a textbook.

**Computational Modeling** (Spring 2017, Fall 2017, Spring 2020, Spring 2021)

*Prerequisites*

- Calculus I

*Topics*

- Python
- data science
- mathematical modeling

*Textbook*

- none (all content is given in Jupyter notebooks)