Course Title
Department/Course
Category/Spacializations
Year Offered
Required or Elective
Semester
Day/Period
Chief Instructor
Instructors

Theme & Objects

Deep Learning
Graduate School of Marine Science and Technology Master's Course<Graduate School Subjects>、<Other Courses' Subjects>
Class
1stCredit
elective
Course TypeFirst Semester
Lecture Room
TUE５
Takenawa Tomoyuki
Takenawa Tomoyuki

102

The aim of this course is to understand theories and to be able to use neural networks, especially deep learning.
Using Python and NumPy, we implement neural networks, backpropagation, optimal methods,
convolutional neural networks.
Lectures and exercises on recurrent neural networks and reinforcement learning are also included.
Google Colaboratory is used as platform.

Learning Outcomes

To understand theories and to be able to use neural networks, especially deep learning.

Styles of Class

combination: online classes(real-time/on-demand)

Course Contents

1. Overview of deep learning
2. Fundamentals of machine learning and information theory
3. Neural networks and error functions
4. Two-layer neural network
5. Multi-layer neural network
6. Optimization in neural networks
7. Regularization in Neural Networks 8.
8. Convolutional neural network (CNN)
9. Development of CNN
10. Generative model and general object detection
11. Recurrent coupled neural network (RNN) 12. natural language processing
12. Natural language processing
13. Reinforcement learning

Prerequisites
Materials of the course are distributed fromhttp://www2.kaiyodai.ac.jp/~takenawa/learning/

Textbook / References

Text: 斎藤 康毅 著「ゼロから作るDeep Learning ―― Pythonで学ぶ
ディープラーニングの理論と実装」オライリージャパン
orIan Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", 2016, MIT Press,
You can read freely at https://www.deeplearningbook.org/

Preparation & Review

Each class session will include about one hour of discussion to deepen your understanding.
In addition, study the following.
It is assumed that students can use Python and NumPy in this class, so students are expected to study thepreparatory materials on the lecture page in advance.
Watch the lecture materials and videos in advance and run the notebooks beforehand for each session.
Answer the questions for the exercises in each session.

Assessment and Examinations

Attendance and commitment to the course 30%
Answers to problems 30%
Final Assignment 40%

Evaluation Criteria

Able to answer questions in lectures.
Able to design Convolutionnal neural networks.

Teaching activities & methods
Instructor Contact Information
Other Information

takenawa@kaiyodai.ac.jp
Lectures are given in Japanese language, but English materials are also provided.
This lecture is held at the same hour with "Deep Learning", a common subject of Master course.
You can only take one of them.

URL

http://www2.kaiyodai.ac.jp/~takenawa/learning/

Code
Teaching Language

EF46V5141JH0
Japanese and English

Workload Calculation(1) Teaching hours in class: 30h(2) Contact hours (Laboratory time): 0h(3) Preparation hours before classes: 25h(4) Review hours after classes: 25h(5) Preparation hours for presentation: 0h(6) Preparation of final assignment: 10h(7) Supervised Study Hours (Meeting with the course instructor or TA): 0h(8) Participation in related seminars: 0h(9) Other activities: 0h(10) Total Work Load: 90h

Related Degree Awarding Policy(1) The ability to understand the social background, academic significance and practical value of research topics andto promote research appropriately: 〇(2) An extensive range of expertise related to basic science and applied science: ◎(3) The ability to explain research results logically, and ethics with regard to academic research: 〇Select 1 to 3 applicable items and delete the non-applicable items. After selecting, plase delete this description.

Relation to SDGs

4 Quality education
9 Industry, innovation and infrastructure