課程名稱 |
深度學習應用於電腦視覺 Deep Learning in Computer Vision |
開課學期 |
112-2 |
授課對象 |
工學院 工學院院學士學位 |
授課教師 |
吳日騰 |
課號 |
CIE5151 |
課程識別碼 |
521EU9310 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期一6(13:20~14:10)星期四5,6(12:20~14:10) |
上課地點 |
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備註 |
本課程以英語授課。教室皆在普405。 限本系所學生(含輔系、雙修生) 總人數上限:50人 |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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課程概述 |
This course introduces the fundamental theory/background knowledge of prevalent machine learning (ML) and computer vision (CV) algorithms. Relevant applications in the broad domain of the engineering community will be introduced to motivate the students. The first half of the semester will focus on the reasoning of artificial intelligence, several ML algorithms, model evaluation, deep learning (DL) and reinforcement learning. The rest of the semester will have emphasis on the reasoning of image processing, image feature extractions and pairing, as well as image-based sensing. After taking this course, students are expected to be equipped with basic knowledge and implementation skills to develop ML, DL or CV based approaches for applications in engineering. |
課程目標 |
Upon taking this course, students are anticipated to be well-prepared in the following items:
1. Understand the fundamental principles that support the ML/DL algorithms.
2. Be able to reasoning the performance of ML/DL models.
3. Be able to implement ML/DL algorithms.
4. Understand the fundamental principles that support the CV algorithms.
5. Understand the image representations of the world.
6. Be able to implement CV algorithms. |
課程要求 |
Prerequisites: Calculus, Computer Programming |
預期每週課後學習時數 |
4hrs |
Office Hours |
每週一 14:30~16:30 備註: Absence of the class will be allowed only if the student informed the instructor in advance. |
指定閱讀 |
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參考書目 |
Several excellent online sources are:
1. A Course in Machine Learning, electronic source available at: http://ciml.info/
2. Christopher Bishop (2006), Pattern Recognition and Machine Learning, Springer
3. Goodfellow et. al (2016), Deep Learning, MIT Press, electronic source available at: https://www.deeplearningbook.org/ |
評量方式 (僅供參考) |
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針對學生困難提供學生調整方式 |
上課形式 |
以錄音輔助, 以錄影輔助, 提供學生彈性出席課程方式 |
作業繳交方式 |
學生與授課老師協議改以其他形式呈現 |
考試形式 |
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其他 |
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週次 |
日期 |
單元主題 |
Week 1 |
2/19, 2/22 |
Introduction to artificial intelligence, machine learning, and deep learning |
Week 2 |
2/26, 2/29 |
Data representations; Evaluation of machine learning models |
Week 3 |
3/4, 3/7 |
Support vector machine |
Week 4 |
3/11, 3/14 |
Support vector machine (Cont.); k-nearest neighbor |
Week 5 |
3/18, 3/21 |
Decision tree; Fully-connected neural network |
Week 6 |
3/25, 3/28 |
Fully-connected neural network (Cont.) |
Week 7 |
4/1, 4/4 |
Introduction to image basics, image-based sensing, image filtering; 4/4 (break) |
Week 8 |
4/8, 4/11 |
Image filtering (Cont.); Convolutional neural network |
Week 9 |
4/15, 4/18 |
Convolutional neural network (Cont.) |
Week 10 |
4/22, 4/25 |
Transfer learning; Auto-encoder |
Week 11 |
4/29, 5/2 |
Generative adversarial network; Midterm (5/2) |
Week 12 |
5/6, 5/9 |
Object classification, detection and segmentation |
Week 13 |
5/13, 5/16 |
Feature extraction and pairing |
Week 14 |
5/20, 5/23 |
Digital image correlation and image stitching |
Week 15 |
5/27, 5/30 |
World-image correspondence |
Week 16 |
6/3, 6/6 |
3D reconstruction (optional) |
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