In this course we will introduce the basic concepts of machine learning, including supervised and unsupervised learning, linear and logistic regression, regularization, neural networks, dimensional reduction, and etc. We will apply the method to physics related problems and learn how to make predictions and solve non-trivial problems. In this course we will introduce the basic concepts of machine learning, including supervised and unsupervised learning, linear and logistic regression, regularization, neural networks, dimensional reduction, and etc. We will apply the method to physics related problems and learn how to make predictions and solve non-trivial problems.
References:
Thoughtful machine learning
O'Relly, Matthew Kirk
http://mropengate.blogspot.com/2015/05/ai-supervised-learning.html
https://happycoder.org/2017/10/07/python-data-science-and-machine-learning-tutorial-introduction/
http://scikit-learn.org/stable/index.html
References:
Thoughtful machine learning
O'Relly, Matthew Kirk
http://mlopengate.blogspot.com/2015/05/ai-supervised-learning.html
https://happycoder.org/2017/10/07/python-data-science-and-machine-learning-tutorial-introduction/
http://scikit-learn.org/stable/index.html
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| on line quizzeson line quizzes online quizzes |
20 | |
| home workshome works home works |
80 |
