基本素養 Basic Literacy
■ 畢業生應具備科技人文素養、資訊工程倫理與終身學習的態度
graduates should equip with the attitude of technological/cultural literacy, information engineering ethics, and life-long learning
□ 畢業生應具備專業外語能力及良好國際觀
graduates should equip with both the professional foreign language proficiency and excellent global view
核心能力 Competence
■ 畢業生應具備資訊專業理論知識
graduates should equip with professional theoretical knowledge in informatics
■ 畢業生應具備資訊專業理論推導、分析、歸納之能力
graduates should equip with the capability of professional theory derivation, analysis, and induction in informatics
■ 畢業生應具備資訊領域獨立發掘問題、策劃實驗、解決問題之能力
graduates should equip with the informatics ability to identify problems independently, to implement experiments, and to solve problems
■ 畢業生應具備資訊領域設計、驗證及實作整合之能力
graduates should equip with the informatics ability in designing, verification, and integrating engineering practices
■ 畢業生應具備資訊領域創新思考之能力
graduates should equip with the informatics capability in innovative planning
□ 畢業生應具備專業簡報及論文撰寫之能力
graduates should equip with the ability in professional presentation and thesis writing
■ 畢業生應具備良好溝通協調與團隊合作之能力
graduates should equip with fair ability in communication, coordination, and team-work collaboration
課程概述 Course Description
這門課除了讓學生能深入了解電腦視覺、機器學習及人工智慧-深度學習的理論知識，與分析深度學習的原理是如何結合人工智慧及電腦視覺發展而來的相互關係外，技術功能面會以授課老師多年的產學合作經驗來舉實際的範例解釋。課程會先教如何從2D影像重建3D物體及增擬實境的基本電腦視覺技術開始，接著就會傳授電腦視覺基本但實用的技術，包括即時偵測、追蹤及辨識系統的設計。再來藉由機器學習的連接帶入深度學習領域，教授如何藉由深度學習的原理來開發更好的即時偵測、追蹤及辨識技術來解決實際的問題。本課程期待培養學生於電腦視覺、機器學習及深度學習領域技術設計及整合實作的能力，透過作業實作來建立學生獨立研究、設計及創新的能力，並可把所學的理論基礎應用到工業界的實務面。空白
課程學習目標 Course Objectives
課程進度 Progress Description
| 進度說明 Progress Description | |
|---|---|
| 1 | Introduction to industry 4.0 - Intelligent robotics and automation: Sensors, machine vision, deep learning, big data and IoT (Internet of Things). |
| 2 | Sensor - Camera calibration: Optimization process and AR (Augmented reality). |
| 3 | Sensor - 3D: 1) Stereo, 2) ToF (Time-Of-Flight, Kinect 2, SoftKinetic), and 3)Structured light (Kinect 1, DLP projector). |
| 4 | From AI (artificial intelligence) to ML (machine learning), to DP (deep learning) |
| 5 | SIFT (and brief HOG): Feature extraction. |
| 6 | Background subtraction/modeling: Real-time motion detection using GMM. |
| 7 | Optical flow: Real-time motion estimation (or feature tracking) for facial expression extraction. |
| 8 | PCA (principal component analysis, dimensionality reduction, domain knowledge) |
| 9 | Midterm exam. (from W01~07) |
| 10 | AdaBoost: Face detection. |
| 11 | SVM (support vector machines): Non-linear classification. |
| 12 | VQ (vector quantization): Clustering and K-means. And HMM (discrete-time hidden Markov model): Facial expression recognition in video. |
| 13 | Deep learning: LeNet, AlexNet, VGG16 and ResNet |
| 14 | Deep learning: Faster R-CNN |
| 15 | Deep learning: RetinaNet |
| 16 | Deep learning: Reinforcement learning |
| 17 | Deep learning: Generative Adversarial Network |
| 18 | Final exam. (from W01~17) |
以上每週進度教師可依上課情況做適度調整。The schedule may be subject to change.
有關課程其他調查 Other Surveys of Courses
1.本課程是否規劃業界教師參與教學或演講? 否Is there any industry specialist invited in this course? How many times? No
2.本課程是否規劃含校外實習(並非參訪)? 否
Are there any internships involved in the course? How many hours? No
3.本課程是否可歸認為學術倫理課程? 否
Is this course recognized as an academic ethics course? In the course how many hours are regarding academic ethics topics? No
4.本課程是否屬進入社區實踐課程?
Is this course recognized as a Community engagement and Service learning course? Which community will be engaged?