人工智慧導論
Introduction to Artificial Intelligence
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 人工智慧導論 EE105（光復） 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
This introductory course provides a comprehensive overview of Artificial Intelligence (AI) with a specific focus on Computer Vision (CV) and Natural Language Processing (NLP). Designed for students without prior experience in AI, this course aims to establish a strong foundation in the fundamental concepts, techniques, and applications of AI, paving the way for advanced studies in generative AI models. Through lectures, hands-on labs, and projects, students will gain practical skills and theoretical insights necessary to understand and work with AI technologies. 這堂課程提供了對人工智慧(AI)的全面概視,特別關注於電腦視覺(CV)和自然語言處理(NLP)。課程設計針對無AI經驗的學生,旨在建立堅實的基礎,涵蓋AI的基本概念、技術和應用,為進階的生成人工智慧模型學習鋪路。透過課程講授、動手實驗和期末專題,學生將獲得實踐技能和理論能力,以理解和運用AI技術。
Python (recommended)/C++, Probability, Calculus, Linear Algebra
無備註
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Midterm: 22% First Three Homework: 36% (12 pts each) Class Participation: 12% Last Two Homework (15 pts each) or Final Project (30 pts)
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Lecture 1: Class Overview and Unsupervised Learning |
| 第 2 週 | Lecture 2: Traditional Classification-Part 1 |
| 第 3 週 | Lecture 3: Traditional Classification-Part 2 |
| 第 4 週 | Lecture 4: Neural Networks Basics |
| 第 5 週 | Hands-on Tutorials on PyTorch |
| 第 6 週 | Lecture 5: Deep Learning in Practice |
| 第 7 週 | Lecture 6: Introduction to Natural Language Processing |
| 第 8 週 | Midterm |
| 第 9 週 | Lecture 7: Deep Learning for NLP+Hands-on Tutorials on NLP |
| 第 10 週 | Lecture 8: Introduction to Computer Vision |
| 第 11 週 | Lecture 9: Advanced Computer Vision |
| 第 12 週 | Lecture 10: Object Detection and Image Segmentation] Hands-on Tutorials on Object Detection |
| 第 13 週 | Lecture 11: Self-Supervised Learning |
| 第 14 週 | Lecture 12: Graph Neural Networks (GNNs) |
| 第 15 週 | Lecture 13: Reinforcement Learning |
| 第 16 週 | Lecture 14: Ethics and Threats of AI |
Eli Stevens, Luca Antiga, and Thomas Viehmann. Deep Learning with PyTorch.
- 地點
- ED-807 or after class
- 時間
- 12:10-13:10 every Tuesday
- 聯絡方式
- TEL: (03) 5712121#54530 EMAIL: hhshuai@nycu.edu.tw