Teaching

 

I have taught four courses since coming to MSU. Three of these courses are special topics courses that are not taught at regular intervals: contact me if a group of students is interested in the course.

 

Competitive Machine Learning (Fall 2020)

This is a course I developing for the fall of this year with some other machine learning faculty at MSU. The special topics course will teach students how to win machine learning contests, like those at Kaggle. Interested students will have some familiarity with machine learning and wish to get the fullest out of the algorithms by competing internationally.

Contact me if you are interested!

 

High Energy-Density Physics (Spring 2020)

This course is taught by University of Michigan by Prof. Kuranz and shared with MSU as a specicial topics course. I am the MSU faculty member for the course, but I act primarily as a local organizer and not an instructor.

 

Applied Machine Learning in the Physical and Life Sciences (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

Image result for hands on machine learning

 

Kinetic Theory (Fall 2018)

Prerequisites

Topics

Textbook

Image result for liboff kinetic theory

 

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)

Prerequisites

Topics

Textbook

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