FRIB-TA Summer School on Machine Learning in Nuclear Experiment and Theory
Matthew Hirn [1]
Morten Hjorth-Jensen [2]
Michelle Kuchera [3]
Raghuram Ramanujan [4]
[1] Department of Mathematics and Department of Computational Science, Mathematics and Engineering, Michigan State University, East Lansing, Michigan, USA
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, Michigan, USA
[3] Physics Department, Davidson College, Davidson, North Carolina, USA
[4] Department of Mathematics and Computer Science, Davidson College, Davidson, North Carolina, USA
Introduction to Data Analysis and Machine Learning
- LaTeX PDF:
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- Jupyter notebook:
Regression Methods
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- Jupyter notebook:
Logistic Regression
- LaTeX PDF:
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- Jupyter notebook:
Gradient Methods and Optimization
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- Jupyter notebook:
Decision trees, from simple to random ones
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Support Vector Machines
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Neural Networks
- LaTeX PDF:
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Convolutional Neural Networks
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Unsupervised Learning, Boltzmann Machines
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Bayesian Statistics and Bayesian Neural Networks
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Recurrent Neural Networks
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Autoencoders
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Reinforcement Learning
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Python and Scikit Learn, a short guide
Practicalities
- Five lectures per day Monday through Wednesday, starting at 830am, see schedule below
- Hands-on sessions in the afternoons till 6pm
- The last day, Thursday, is dedicated to solving explicit problems with hands-on guidance and relevant examples.
Recommended textbook
General learning book on statistical analysis:
- Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
- Peter Hoff, A first course in Bayesian statistical models, Springer
- Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer
General Machine Learning Books:
- Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
- David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
- David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
Schedule
Lectures are 50 min and there is a small break of 10 min between each lecture. Longer breaks at 1030am-11am and 3pm-330pm.
Acronyms for teachers
- MH = Matthew Hirn
- MHJ = Morten Hjorth-Jensen
- MK = Michelle Kuchera
- RR = Raghuram Ramanujan
Monday May 20, 2019: (breaks of 10 min for every lecture)
- 8am-830am: Welcome and registration
- 830am-930am: Introduction to Machine Learning and various Python packages (MHJ)
- 930am-1030am: Linear Regression (MHJ)
- 1030am-11am: Break, coffee, tea etc
- 11am-12pm: Logistic Regression (MHJ)
- 12pm-1pm: Lunch
- 1pm-2pm: Optimization of functions, gradient descent and stochastic gradient descent (MHJ)
- 2pm-3pm: Decision Trees and Random Forests (MHJ)
- 3pm-330pm: Break, coffee, tea etc
- 330pm-6pm: Hands-on sessions with selected Physics examples
Tuesday May 21, 2019:
- 830am-930am: Neural networks (MK and RR)
- 930am-1030am: Neural networks and deep learning (MK and RR)
- 1030am-11am: Break, coffee, tea etc
- 11am-12pm: Convolutional Neural Networks (CNNs) and examples from nuclear physics experiments (MK and RR)
- 12pm-1pm: Lunch
- 1pm-2pm: CNNs (MK and RR)
- 2pm-3pm: Autoenconders and recurrent neural networks and examples from nuclear physics experiments (MK and RR)
- 3pm-330pm: Break, coffee, tea etc
- 330pm-6pm: Hands-on sessions with examples from nuclear physics experiments
Wednesday May 22, 2019:
- 830am-930am: Reinforcement learning (MK)
- 930am-1030am: Introduction to exploratory data analysis and unsupervised learning: PCA (MH)
- 1030am-11am: Break, coffee, tea etc
- 11am-12pm: Clustering and introduction to nonlinear dimension reduction: k-means and t-SNE (MH)
- 12pm-1pm: Lunch
- 1pm-2pm: Nonlinear dimension reduction: Spectral graph theory and manifold learning (MH)
- 2pm-3pm: Boltzmann machines and many-body problems (MHJ)
- 3pm-330pm: Break, coffee, tea etc
- 3pm-6pm: Hands-on sessions with examples from quantum mechanical many-body problems
Thursday May 23, 2019:
- 9am-10am: Current state of Machine Learning research (MK)
- 10am-12pm: Hands-on sessions with examples from nuclear physics, experiment and theory
- Coffee, tea etc at 1030am
- 12pm-1pm: Lunch
- 1pm-5pm: Hands-on sessions with examples from nuclear physics, experiment and theory.
- Coffee, tea, etc at 3pm