2025 (Current Year) Faculty Courses School of Computing Major courses
Fundamentals of Progressive Artificial Intelligence
- Academic unit or major
- Major courses
- Instructor(s)
- Kei Miyazaki / Norio Tomii / Naoaki Okazaki / Masamichi Shimosaka / Masakazu Sekijima / Katsumi Nitta / Yoshihiro Miyake / Isao Ono
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- XCO.T679
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Mar 19, 2025
- Language
- English
Syllabus
Course overview and goals
Artificial Intelligence is a research area that aims at artificially creating intelligence like humans. In recent years, artificial
intelligence was successfully applied to various domains with the advances on machine learning and deep learning utilizing big data and computation power. This lecture expects students to acquire knowledge that is essential for creating applications of artificial intelligence, explaining basic concepts and theories of artificial intelligence.
Course description and aims
Students will be able to acquire knowledge that is essential for creating applications of artificial intelligence.
Keywords
classification, regression, gradient-based method, perceptron, activation function, backpropagation, automatic differentiation,
convolutional neural network
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
All classes are given as Zoom lectures.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance | Artificial Intelligence applied to the real world |
| Class 2 | Essential Mathematics for Machine Learning | Linear Algebra, Probability Theory and Statistics, Calculus |
| Class 3 | Linear Regression | Loss function, empirical risk minimization, overfitting，regularization，bias and variance，linear model (regression)，ridge regression |
| Class 4 | Linear Classification | Linear model (classification)，logistic regression, gradient methods |
| Class 5 | Single-layer Neural Network | single-layer perceptron, activation functions, computational graph, automatic differentiation |
| Class 6 | Multi-layer Neural Network | multi-layer perceptron, hidden units, backpropagation, softmax function |
| Class 7 | Convolutional Neural Network | convolutional neural network, dropout |
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.
Course materials are distributed via Science Tokyo LMS.
Evaluation methods and criteria
Based on multiple times of the reports and the final report
Related courses
- XCO.T680 ： Exercises in Fundamentals of Progressive Artificial Intelligence
- XCO.T687 ： Progressive Applied Artificial Intelligence and Data Science A
- XCO.T688 ： Progressive Applied Artificial Intelligence and Data Science B
- XCO.T689 ： Progressive Applied Artificial Intelligence and Data Science C
- XCO.T690 ： Progressive Applied Artificial Intelligence and Data Science D
- XCO.T677 ： Fundamentals of Progressive Data Science
- XCO.T678 ： Exercises in Fundamentals of Progressive Data Science
Prerequisites
Basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics is required.
This course is given for the doctor course students. To register both this course and XCOT.T489 at once is not allowed.
Attendance is mandatory from the first lecture class, as the course will cover matters necessary for understanding the subsequent lecture classes, in addition to guidance.