機器學習導論
Introduction to Machine Learning
| 節 | 週三 | 週五 |
|---|---|---|
2 09:00–09:50 | 機器學習導論 ED303（光復） | |
7 15:30–16:20 | 機器學習導論 ED303（光復） 2 節連堂 | |
8 16:30–17:20 |
* 根據陽明交大上課時間表所列
Learn the basic principles and some popular methods of machine learning 1. Introduction 2. Math Background 3. Supervised Learning (Linear Models, Neural Networks, Kernel Methods) 4. Unsupervised Learning (Mixture Models, EM Algorithm) 5. Models for Sequential Data (HMM) 6. Deep Learning (Deep Discriminative Models, Deep Generative Models) 7. Self-supervised Learning 8. Reinforcement Learning
Required: Probability, Linear Algebra Recommended: Random Processes Coding Skill: Familar with C/C++ or Python (Both homework assignments and final project involve coding efforts)
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Course Lectures: You may either come to the calssroom or listen to the lectures via Google Meet. Wednesday (3:30pm-5:20pm) ED301 (Live Broadcast: https://meet.google.com/wwy-yyus-ckv) Friday (9:00am - 9:50am) ED303 (Live Broadcast: https://meet.google.com/hes-bfxy-egm) To be announced at NYCU E3 platform
Homework 45% Final Project 25% Due: 2024/6/21 (Friday) Final Exam 30% 3:30pm - 5:30pm. 2024/6/5 (Wed)
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(Optional) Pattern Recognition and Machine Learning, by Christopher M. Bishop, Springer, 2007.
- 地點
- Room 649, Engineering Building IV
- 時間
- TBD
- 聯絡方式
- shengjyh@nycu.edu.tw