| 学部・研究科Faculty/Graduate school | 理工学研究科Graduate School of Science and Engineering |
| 添付ファイル名Attached documents | |
| 年度Year | 2023 |
| 授業コードClass code | YB039 |
| 旧授業コードPrevious Class code | |
| 旧科目名Previous Class title | |
| 開講時期Term | 秋学期授業/Fall |
| 曜日・時限Day/Period | 金4/Fri.4 |
| 科目種別Class Type | |
| キャンパスCampus | 小金井 |
| 教室名称Classroom name | 各学部・研究科等の時間割等で確認 |
| 配当年次Grade | |
| 単位数Credit(s) | 2 |
| 備考（履修条件等）Notes | |
| 実務経験のある教員による授業科目Class taught by instructors with practical experience | |
| カテゴリーCategory | 応用情報工学専攻 |
【Outline (in English)】
This course is an introduction to deep learning and its applications mainly for computer vision. The course includes the fundamental components of modern deep learning systems such as neural network architectures and learning algorithms. Later, some research topics for computer vision such as image classification, object detection, image segmentation, generative models (GANs), attention & transformers, etc., will be covered.
【Default language used in class】
英語 / English
【授業の概要と目的（何を学ぶか） / Outline and objectives】
This course is an introduction to deep learning and its applications mainly for computer vision. The course includes the fundamental components of modern deep learning systems such as neural network architectures and learning algorithms. Later, some research topics for computer vision such as image classification, object detection, image segmentation, generative models (GANs), attention & transformers, etc., will be covered.
【到達目標 / Goal】
After this course, students will be able to understand, implement and/or apply deep neural networks to their research. In addition, they will gain a better overview of several important research topics in computer vision.
【この授業を履修することで学部等のディプロマポリシーに示されたどの能力を習得することができるか（該当授業科目と学位授与方針に明示された学習成果との関連） / Which item of the diploma policy will be obtained by taking this class?】
ディプロマポリシーのうち、「DP1」「DP2」「DP3」に関連
【授業で使用する言語 / Default language used in class】
英語 / English
【授業の進め方と方法 / Method(s)】
（学期の途中で変更になる場合には、別途提示します。 /If the Method(s) is changed, we will announce the details of any changes. ）
Lectures will be highly interactive. Students will be asked to participate in discussions and ask questions about the lectures.
There will be some deep learning implementations given and students will practice together during the class. The assignment will be in a form of coding practice or questions.
Students will be asked to explore and present their understanding of a topic/project they are interested in deep learning as a final presentation.
【アクティブラーニング（グループディスカッション、ディベート等）の実施 / Active learning in class (Group discussion, Debate.etc.)】
あり / Yes
【フィールドワーク（学外での実習等）の実施 / Fieldwork in class】
あり / Yes
【授業計画 / Schedule】
授業形態 / methods of teaching：対面/face to face
※各回の授業形態は予定です。教員の指示に従ってください。
| 回 / No. | 各回の授業形態予定 / methods of teaching | テーマ / Theme | 内容 / Contents |
|---|---|---|---|
| 1 | 対面/face to face | Course introduction | Course overview, history of computer vision, setting up coding environments |
| 2 | 対面/face to face | Basic machine learning review | Linear classification, model representation, loss functions, and basic mathematics review |
| 3 | 対面/face to face | Optimization | Stochastic gradient descent (SGD), regularization, weight decay, momentum, etc. |
| 4 | 対面/face to face | Neural networks (part I) | Introduction to neurons and the brain, fully-connected networks, forward propagation |
| 5 | 対面/face to face | Neural networks (part II) | Backward propagation algorithm |
| 6 | 対面/face to face | Training tricks for neural networks | Study several activation functions, normalization, and regularizations techniques |
| 7 | 対面/face to face | Convolutional neural networks (CNN) | Introduction to convolutional neural networks, introduce some CNN models for image classification |
| 8 | 対面/face to face | Visualizing and understanding models | Feature visualization, understanding the decisions of models, neural style transfer |
| 9 | 対面/face to face | Object detection and image segmentation | Study various CNN architectures for object detection and image segmentation |
| 10 | オンライン/online | Generative adversarial networks | Introduction to generative adversarial networks and its applications |
| 11 | オンライン/online | Attention and transformers | Introduction to self-attention and transformers and the applications for computer vision |
| 12 | オンライン/online | Self-supervised learning | Introduction to deep self-supervised learning, image inpainting, contrastive learning methods |
| 13 | オンライン/online | Diffusion Models | Introduction to diffusion models and its applications |
| 14 | オンライン/online | Final presentations | Students present their projects |
【授業時間外の学習（準備学習・復習・宿題等） / Work to be done outside of class (preparation, etc.)】
【本授業の準備・復習時間は、各４時間を標準とします。】Homework and final presentation/project
【テキスト（教科書） / Textbooks】
Slides will be printed and distributed
【参考書 / References】
- “Deep Learning” by Goodfellow, Bengio, and Courville (available online at https://www.deeplearningbook.org/)
- CS231n: Deep Learning for Computer Vision - Stanford University (available online at http://cs231n.stanford.edu/index.html)
【成績評価の方法と基準 / Grading criteria】
Assignment (both in class/homework) 50% + Final presentation/project 50%
【学生の意見等からの気づき / Changes following student comments】
Not applicable
【学生が準備すべき機器他 / Equipment student needs to prepare】
- Personal laptop
- Basic knowledge of Machine Learning
- Basic knowledge of Python, and some packages such as Numpy, Pandas, Matplotlib, PyTorch, etc.