2020 Faculty Courses School of Computing Major courses
Fundamentals of artificial intelligence
- Academic unit or major
- Major courses
- Instructor(s)
- Naoaki Okazaki / Masamichi Shimosaka / Nakamasa Inoue / Katsumi Nitta
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Mon (Zoom)
- Class
- -
- Course Code
- XCO.T489
- Number of credits
- 100
- Course offered
- 2020
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 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 in both Ookayama and Suzukakedai campuses with the use of video conference systems.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Class 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 |
| Class 8 | Final examination |
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.
Cource materials are distributed via OCW-i.
Evaluation methods and criteria
Based on the final exam.
Related courses
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： FinTech and Data Science
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
Prerequisites
Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.