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.
讲师
日期
2024年03月06日 至 05月29日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周三 | 12:30 - 15:55 | A3-2-201 | ZOOM 06 | 537 192 5549 | BIMSA |
课程大纲
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
听众
Undergraduate
, Advanced Undergraduate
, Graduate
视频公开
公开
笔记公开
公开
语言
英文
讲师介绍
Shailesh Lal于Harish Chandra研究所获得博士学位。他的研究兴趣是机器学习在弦理论和数学物理中的应用，弦理论中的黑洞和higher-spin holography。