Practical Machine Learning for Theoretical Physics
The course is targeted to those who know beginning graduate level physics but do not know machine learning. We will cover important methods in machine learning with a view to their applications to current physics such as string theory, particle physics, critical phenomena, gravitational waves and integrability. We will also cover some applications to Lie algebras. We will use Python3, scikit-learn and Keras/Tensorflow. These will be introduced in the lectures.
Lecturer
Date
6th March ~ 29th May, 2024
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Wednesday | 12:30 - 15:55 | A3-2-201 | ZOOM 06 | 537 192 5549 | BIMSA |
Syllabus
1. Linear Regression and Generalized Linear Models
2. Logistic Regression
3. Constructing and Validating Models: Bias, Variance and Validation
4. K nearest neighbours, Classification and Regression
5. Decision Trees
6. Boosting and Bagging, Boosted Trees.
7. Support Vector Machines
8. Neural Networks, Multi-Layer Perceptrons
9. Convolutional Neural Networks
10. Unsupervised Learning: Clustering, Dimensionality Reduction
11. Generative Models
2. Logistic Regression
3. Constructing and Validating Models: Bias, Variance and Validation
4. K nearest neighbours, Classification and Regression
5. Decision Trees
6. Boosting and Bagging, Boosted Trees.
7. Support Vector Machines
8. Neural Networks, Multi-Layer Perceptrons
9. Convolutional Neural Networks
10. Unsupervised Learning: Clustering, Dimensionality Reduction
11. Generative Models
Audience
Undergraduate
, Advanced Undergraduate
, Graduate
Video Public
Yes
Notes Public
Yes
Language
English
Lecturer Intro
Dr Shailesh Lal received his PhD from the Harish-Chandra Research Institute. His research interests are applications of machine learning to string theory and mathematical physics, black holes in string theory and higher-spin holography.