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- Course: PHYS 398MLA
- Instructor: Prof. Mark Neubauer, msn@illinois.edu
- TA: Dewen Zhong, dzhong6@illinois.edu
- Lectures: Mondays from 3-4:50 pm in 222 Loomis Laboratory of Physics
- NOTE: Due to a room conflict, the first lecture will be in 322 Loomis
- Need help?
Welcome you to the Data Analysis and Machine Learning Application (for physicists) course!
In this course, you will learn fundamentals of how to analyze and interpret scientific data and apply modern machine learning tools and techniques to problems common in physics research such as classification and regression. This course offering is very timely given the explosion of interest and rapid development of data science and artificial intelligence. Every day there are new applications of machine learning to the physical sciences in ways that are advancing our knowledge of nature.
This course is designed to be interactive and collaborative, employing Active Learning methods, at the same time developing your own skills and knowledge (and course grade :). I initiated this course out of my view that we live in an increasingly data-centric world, with both people and machines learning from vast amounts of data. There has never been a time where early-career physicists were more in need of a solid understanding in the basics of scientific data analysis, data-driven inference and machine learning, and a working knowledge of the most important tools and techniques from modern data science than today.
This is the second offering of a new course and unlike any that is being taught in our Department. As such, I ask for your feedback on any aspect of the course so that I can work to improve the curriculum.
- You need a laptop for this course. It is assumed that you have a laptop running MacOS, Linux or Windows for use both inside and outside of the class.
- Some knowledge of python preferred but not required. You do need to have a working knowledge of the basics of computer programming.
- There is some setup required to ensure a consistent and functioning software environment on your computer to use the Jupyter notebooks in this course. This setup is detailed here and is best started before the first lecture to work out any wrinkles so that we can get started on the physics and data science content of the course.
Topics covered include:
- Notebooks and numerical python
- Handling and Visualizing Data
- Finding structure in data
- Measuring and reducing dimensionality
- Adapting linear methods to nonlinear problems
- Estimating probability density
- Probability theory
- Statistical methods
- Bayesian statistics
- Markov-chain Monte Carlo in practice
- Stochastic processes and Markov-chain theory
- Variational inference
- Optimization
- Computational graphs and probabilistic programming
- Bayesian model selection
- Learning in a probabilistic context
- Supervised learning in Scikit-Learn
- Cross validation
- Neural networks
- Deep learning
Topics will be demonstrated in-class through live-code examples/slides in Juypter notebooks, available at syllabus/notebooks.
The lectures will include physics and data science pedagogy, demonstrated through live examples in Jupyter notebooks that you will work through in class. You are required to attend each lecture with your laptop and working environment. Attendance will be taken.
Homework is an important part of the course where you will have an opportunity to apply the techniques you are learning to problems relevant to the analysis of scientific data. All assignments are listed within the Course Outline. You will submit your homework via your private Github repository.
- Class Participation: ~20%
- Homework: ~45%
- Research project: ~35%
- Getting overview of the course, including reading list and homework assignments
- Setting up your environment
- Complete setting up your environment so that you can launch and execute notebooks
- None
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You can find the references list, including required and recommended reading, at Reading list
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Some quick reference guides
- Linux Bash Shell
- Github
- Conda
- Python
- Markdown
- Jupyter Notebooks: Interface, Keyboard shortcuts
- Sharing code snippets: gist.github.com
- Asking questions of broader development community: Stack Overflow
I found the Atom editor to be the best option. It has full Github integration which avoids having to type git commands every time
With a plug-in, it also does latex syntax highlighting. Install it with:
apm install latex
apm install language-latex
I would like to acknowledge David Kirby at the University of California at Irvine for the materials and setup for which this course is based and the helpful discussions we have had. I would like to thank Matthew Feickert and Dewen Zhong for their guidance and contributions to the course. I also acknowledge the course at github.com/advanced-js for which the syllabus template was utilized.
Material for a University of Illinois course offered by the Physics Department.
Content is maintained on github and distributed under a BSD3 license.
