2024 Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Advanced Data Science and Artificial Intelligence 1
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Isao Ono / Nakamasa Inoue / Hiroaki Yamada / Katsumi Nitta / Yoshihiro Miyake
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Wed
- Class
- -
- Course Code
- DSA.A501
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
Deep learning is one of the artificial intelligence techniques using multi-layer neural networks, and has produced significant results in various fields such as image recognition, speech recognition, and natural language processing. In this course, we teach representative deep learning methods and their applications, which are important for researchers and engineers in science and engineering fields. The course deals with advanced topics that are not covered in the courses of Fundamentals of Artificial Intelligence and Fundamentals of progressive Artificial Intelligence.
Course description and aims
The goal is to understand typical algorithms of deep learning and their applications.
Keywords
Deep learning，CNN，VAE，GAN，RNN，LSTM，Attention，Transformer，Deep reinforcement learning
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
ZOOM is used to allow students to take courses at Ookayama or Suzukakedai campuses.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | CNN, VAE, GAN and their applications to speech and image recognition (1) | Understanding CNN, VAE, GAN and their applications to speech and image recognition. |
| Class 2 | CNN, VAE, GAN and their applications to speech and image recognition (2) | Understanding CNN, VAE, GAN and their applications to speech and image recognition. |
| Class 3 | RNN, LSTM, Attention, Transformer and their applications to natural language processing and speech recognition (1) | Understanding RNN, LSTM, Attention, Transformer and their applications to natural language processing and speech recognition. |
| Class 4 | RNN, LSTM, Attention, Transformer and their applications to natural language processing and speech recognition (2) | Understanding RNN, LSTM, Attention, Transformer and their applications to natural language processing and speech recognition. |
| Class 5 | Deep Reinforcement Learning and Its Applications to Robot Control and Natural Language Processing (1) | Understanding deep reinforcement learning and its applications to robot control and natural language processing. |
| Class 6 | Deep Reinforcement Learning and Its Applications to Robot Control and Natural Language Processing (2) | Understanding deep reinforcement learning and its applications to robot control and natural language processing. |
| Class 7 | Deep Reinforcement Learning and Its Applications to Robot Control and Natural Language Processing (3) | Understanding deep reinforcement learning and its applications to robot control and natural language processing. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None
Reference books, course materials, etc.
Distributed electronically at T2SCHOLA.
Evaluation methods and criteria
Evaluation is based on in-class assignments and reports.
Related courses
- Fundamentals of data science（XCO.T487）
- Exercises in fundamentals of data science（XCO.T488）
- Fundamentals of artificial intelligence（XCO.T489）
- Exercises in fundamentals of artificial intelligence（XCO.T490）
Prerequisites
- Students should have basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics.
- Students should have basic programming skills in Python.
- Students should be able to understand the content taught in Fundamentals of Artificial Intelligence or Fundamentals of Progressive Artificial Intelligence, as well as in Exercises in Fundamentals of Artificial Intelligence or Exercises in Fundamentals of Progressive Artificial Intelligence.