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Machine Learning in Physics

This is to facilitate the “Machine Learning in Physics” course that I am teaching at Sharif University of Technology for winter-20 semester. For more information, see the course page at

http://sharif.edu/~sraeisi/ML

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Reading Materials

• Mehta, Pankaj, et al. "A high-bias, low-variance introduction to machine learning for physicists." Physics Reports (2019).

• Nielsen, Michael A. Neural networks and deep learning. Vol. 25. San Francisco, CA, USA:: Determination press, 2015. (Available online )

• Chollet, François, "Deep Learning with Python." (2018).

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Course Materials

The course material is posted here. If you come across a mistake or problem, please let me know.

Also, the videos of some(most) of the lectures are posted here. These videos are in Farsi.

Chapter 1: Basics of Machine Learning

Topic	Lecture notes	Notebook(s)
Introduction: Dipping a toe in the water	Introduction to ML
Clustering	Clustering
Regression and Classification	Regression and Classification

Chapter 2: Data

Topic	Lecture notes	Notebook(s)
Data: the basics	Data

Chapter 3: Model Selection and Evaluation

Topic	Lecture notes	Notebook(s)
Model Evaluation	Metrics
Model Selection	Statisticl Learning

Chapter 4: Neural Networks

Topic	Lecture notes	Notebook(s)
Introduction	Intro
Feedforward	Feedforward
Training: Back-propagation	Back-propagation

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Project

Milestone	Due date	Submission Link
Data	March 20th	Submit here.
Data	April 20th	Submit here.

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Requirements:

• Decent understanding of programming and python and the following libraries

  • Numpy

  • Pandas

  • Plotting and graphical presentation tools in python

• Git and Github (if you not familiar, let me know.)

• Basic understanding of quantum mechanics and statistics.

• Basic understanding of machine learning

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Marking Scheme

This is a tentative plan and we may change it as we move on.

• Course Project: 40%

• Assignments: 30%

• In-class exercises 10%

• Final exam (set for Thursday, June 20th, 9AM): 30%

These add up to 110% which include the bonus as well.

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Course Projects

This is a group project and counts towards 40% of the final grade.

The idea is that each group decides on a project at the beginning of the course and apply everything that we cover to their project. Here are some of the expectations for the course project:

• Some initial proposal: Clear statement of the problem and some primary assessment of why using ML could help answer this problem. (Due Feb 28th)

• Data collection/generation and preparation: (Due March 15th => Extended to March 20th )

  • Create a folder for this part
  • Have a description (readme file) for the data
  • Describe your data: Where it comes from, different feautres and their physical significance, your target value(s)
  • Create a notebook and implement the following in different sections:
    • Clean up the data (remove the missing data and convert everything to numerical values)
    • Scale your data
    • Analysis of features and target (Histograms and )
    • Feature selection (Try different techniques and assess how well they work on your data)
    • Feature extraction (Try different techniques and assess how well they work on your data)

• Application of the basic ML techniques: (Due April 15th)

  • A table of assessment (Will give an example later.)

  • Investigation of variance and bias of the techniques investigated.

  • Learning and validation curves

• Application of NN and setting the hyperparameters (Due April 30th)

• Oral presentation (See me to set up the time, it should be before June 24th.)

• Written term paper (It should be submitted by July 5th.)

Some notes:

• Make sure you include citations to all the resources you use!
• You should submit your work as a group rather than separate individual submissions.
• Scripts, notebooks and figures without description would not count toward your grade.
• Your codes should include enough comments and information that can be easily followed.
• It is essential that all group members contribute (make commits) to their repositories, this is the only way I can make sure that everyone participated in their project.

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Cheat sheets and guides

See the files in the CheatSheet folder.

Item	Description
Jupyter	Jupyter provides an interactive environment for programming. We will be mostly using the python 3 kernel.
Git and Github	Git provides a strong infrastructure for version control. Github is web-based hosting service for version control and it also provides services for collaboration.
Python	It is the programming language that we will be mostly using for this course.
NumPy	It’s a python library that provides strong and efficient tools for manipulation of high-dimensional arrays.
SciPy	It’s a python library, built on NumPy for mathematical and scientific computing.
Pandas_basics Pandas 2 Importing data	It’s a python library, built on NumPy that provides efficient tools for handling and analysis of data.
Matplotlib Seaborn	These are two of the most common python library for visualization.
Scikit-Learn	It’s a python library that provides a nice and fairly efficient implementation of most the machine learning techniques and ideas.
Keras	It is python library that provides a high-level and easy-to-use interface for Tensorflow and some other deep learning libraries.