機器人之機率統計與深度學習技法
Statistical Techniques and Deep Learning in Robotics
| 節 | 週四 |
|---|---|
2 09:00–09:50 | 機器人之機率統計與深度學習技法 EE635 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course is designed for graduates who want to learn the key statistical techniques and deep learning based approaches in robotics and/or want to become robotics scientists or engineers. The course will cover the cutting-edge approaches for solving the state estimation, mapping, localization, detection, tracking, forecasting, policy generation, behavior design and system integration problems in robotics.
This is an advanced course describing the key statistical techniques and deep learning based approaches in robotics. This course is highly related to robotics, computer vision, machine learning and deep learning. The students must have good C, C++, Python programming skills and the students should have some hands-on experiences on robotics, computer vision or machine learning.
學期作業: hands-on experiments, project report and presentation and competitions. 考試狀況: one midterm exam. 評量方法: assignments (30%), midterm exam (20%), projects (20%), and competitions (20%) and class participation (10%).
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Statistical Techniques |
| 第 3 週 | Deep Learning Techniques |
| 第 4 週 | State Estimation |
| 第 5 週 | State Estimation |
| 第 6 週 | State Estimation |
| 第 7 週 | Mapping |
| 第 8 週 | Midterm |
| 第 9 週 | Mapping |
| 第 10 週 | Perception |
| 第 11 週 | Perception |
| 第 12 週 | Perception |
| 第 13 週 | Action |
| 第 14 週 | Action |
| 第 15 週 | Action |
| 第 16 週 | Final Competition |
- Probabilistic Robotics by Thrun, Burgard and Fox. MIT Press, 2005. - Computer Vision: Models, Learning and Inference by Prince. Cambridge Univ. Press, 2012. - Deep Learning by Goodfellow, Bengio and Courville. MIT Press, 2016.
- 地點
- Room 766, Engineering 5 Building or online.
- 時間
- Make an appointment by email.
- 聯絡方式
- bobwang@ieee.org