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UCSD PHYS 139/239: Machine Learning in Physics
Week 1
Lecture 01: Introduction
Lecture 02: Perceptron and stochastic gradient descent
Hands-on 01a: Introduction
Hands-on 01b: Debugging
Homework 1 (due Week 2)
Week 2
Lecture 03: Support vector machine and logistic regression
Lecture 04: (Boosted) decision trees
Hands-on 02: Tabular data and BDTs: Classifying LHC collisions
Week 3
Lecture 05: Neural networks
Lecture 06: Optimizing neural networks
Hands-on 03: Tabular data and NNs: Classifying particle jets
Homework 2 (due Week 4)
Week 4
Lecture 07: Convolutional neural networks
Lecture 08: Advanced convolutional neural networks
Hands-on 04: Image data and CNNs: Classifying astronomical images
Week 5
Lecture 09: Time series and recurrent neural networks
Lecture 10: More recurrent neural networks
Hands-on 05: Time series data and RNNs: Identifying cosmic rays in radio signals
Homework 3 (due Week 6)
Week 6
Lecture 11: Graph neural networks
Lecture 12: More graph neural networks and transformers
Hands-on 06: Graph data and GNNs: Tagging Higgs boson jets
Week 7
Lecture 13: Unsupervised learning
Lecture 14: More autoencoers
Hands-on 07: Autoencoders for anomaly detection
Homework 4 (due Week 8)
Week 8
Lecture 15: Model compression
Lecture 16: Knowledge distillation
Hands-on 08: Model Compression
Week 9
(Guest) Lecture 17: Generative modeling by Dr. Benjamin Nachman
(Guest) Lecture 18: Equivariant neural networks by Prof. Rose Yu and Jianke Yang
Week 10
(Guest) Lecture 19: Physics-informed neural networks by Dr. Amir Gholami
Finals Week
Final Projects
References
References
Repository
Open issue
Index
