機器學習
Machine Learning
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 機器學習 EE206 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
In this course, to enable students to understand the application of machine learning and the theoretical background of machine learning and evaluate how to use appropriate algorithms in practical examples. Topics cover regression analysis in data prediction, data dimensionality reduction methods in data feature extraction, and neural network-like methods in data classification.
Linear algebra, probability, fundamental calculus, programming (Python, Matlab, Tensorflow, etc.), optimization EECN30124), highly recommended!
TA: Satrio Sanjaya (sanjaya.sanjaya5@gmail.com)
Four homework assignments 20% Mid-term exam 30% Final exam 30% Final project 20%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Intro & probability & optimization & scikit learn |
| 第 2 週 | No class |
| 第 3 週 | Holiday |
| 第 4 週 | Fundamentals of ML |
| 第 5 週 | Parametric methods |
| 第 6 週 | Regression |
| 第 7 週 | Dimension reduction |
| 第 8 週 | Clustering |
| 第 9 週 | Mid-term |
| 第 10 週 | Logistic regression & Decision Tree |
| 第 11 週 | Kernel machines |
| 第 12 週 | Neural Networks |
| 第 13 週 | Hidden Markov Models |
| 第 14 週 | Ensemble methods |
| 第 15 週 | Reinforcement learning |
| 第 16 週 | Introduction to Scikit-Learn |
| 第 17 週 | Final exam |
| 第 18 週 |
1. E. Alpaydin (2004). Introduction to machine learning. Cambridge, MA: MIT Press. 2. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018. 3. C. Bishop "Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. 2006. corr. 2nd printing edn." (2007). 4. Sergios Theodoridis (2015). Machine Learning: A Bayesian and Optimization Perspective. Elsevier Ltd. 5. Sebastian Raschka (2015), Python machine learning. Packt publishing ltd.
- 地點
- EE749
- 時間
- Monday, 11am-12pm
- 聯絡方式
- Tel: 03-5712121 ext54345 or Email: sofin@nycu.edu.tw