2025 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence 2
Basics and Applications of Data Science and Artificial Intelligence II
- Academic unit or major
- Center of Data Science and Artificial Intelligence 2
- Instructor(s)
- Kei Miyazaki / Katsumi Nitta / Norio Tomii / Keiji Okumura / Jun Sakuma / Isao Ono / Yoshihiro Miyake
- Class Format
- Lecture/Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- -
- Course Code
- DSA.B202
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course gives basic theories, methods, and algorithms of data science, data engineering, and AI to students those who have completed the course "Basics and Applications of Data Science and Artificial Intelligence I" in the first quarter. The curriculum is designed so that it provides an intermediate-level course study of data science and AI between literacy- and expert-level ones. The course would enable students to understand theories and methods deeply and achieve practical skills in problem solving through a variety of examples and exercises.
Course description and aims
Students will be able to:
1) Understand significance of studying data science, as well as data analysis methods, and choose appropriate data analysis and visualization methods.
2) Understand roles of data engineering, representation methods of various data on a computer, and data acquisition/processing/accumulation techniques.
3) Understand history of AI, its technical background, AI ethics, machine learning and learning algorithms, neural networks and deep learning algorithms, and apply AI technology to problem solving.
Keywords
Population, inferential statistics, statistical interference, statistical test, unsupervised leaning, supervised leaning, reinforcement learning, DNN, CNN, RNN, deep generative model, VAE, GAN, transformer, recognition, prediction
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
To check students’ understanding, students are assigned exercises at every class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Fundamentals of mathematical statistics, part 1 | Understand basics of mathematical statistics and also learn statistical interference methods through specific examples. |
| Class 2 | Fundamentals of mathematical statistics, part 2 | Learn the basic theory of hypothesis testing and understand hypothesis testing methods through specific examples. |
| Class 3 | Machine learning, part 1 | Understand basics of machine learning algorithms by applying them to practical problems such as classification and clustering. |
| Class 4 | Machine learning, part 2 | Understand basics of machine learning algorithms in which topics include supervised learning (regression, classification), cross validation and regularization. |
| Class 5 | Neural networks and deep learning, part 1 | Understand principles of artificial neural networks and their training algorithms in which topics includes perceptron, multilayer perceptron, and back propagation algorithm. |
| Class 6 | Neural networks and deep learning, part 2 | Understand structures and mechanisms of useful neural networks such as deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). |
| Class 7 | Applications of machine learning (especially reinforcement learning), deep learning and AI | Understand roles of AI technology in our daily life by looking at a wide variety of examples to which AI technology has been successfully applied. |
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. Lecture materials and exercise materials will be given in the class.
Reference books, course materials, etc.
Lecture materials and exercise materials will be found on Science Tokyo LMS in advance.
Evaluation methods and criteria
Grading is based on exercises and term-end report.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
- LAS.I131 ： Basics of Data Science and Artificial Intelligence
- LAS.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- LAS.M105 ： Calculus II
- LAS.M106 ： Linear Algebra II
- XCO.T281 ： Basics and Applications of Data Science and Artificial Intelligence I
Prerequisites
Students are assumed to have the knowledge given in Calculus I and II, Linear Algebra I and II, Computer Science I, and Basics and Applications of Data Science and Artificial Intelligence I.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
MIYAZAKI, Kei (lecture_ba_2025[at]dsai.isct.ac.jp)
Office hours
The instructor and TAs will accept questions in person or via Zoom immediately after the lecture. After the lecture, questions should be asked via e-mail.
Other
Science and engineering fields students will attend in person, while medical and dental fields students will participate live via Zoom.