2024 Faculty Courses School of Computing Major courses
Exercises in Fundamentals of Progressive Artificial Intelligence
- Academic unit or major
- Major courses
- Instructor(s)
- Kei Miyazaki / Norio Tomii / Keisuke Yanagisawa / Naoaki Okazaki / Masamichi Shimosaka / Masakazu Sekijima / Katsumi Nitta / Yoshihiro Miyake / Isao Ono
- Class Format
- Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon
- Class
- -
- Course Code
- XCO.T680
- Number of credits
- 010
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 17, 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 skills that is essential for creating applications of artificial intelligence, implementing basic concepts and theories as a computer program.
Course description and aims
Students will be able to acquire skills that is essential for creating applications of artificial intelligence, experiencing data processing and machine learning on computers.
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
In class, students are required to solve exercises that are linked with the contents of taught course "XCO. 679 Fundamentals of Progressive Artificial Intelligence".
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Class guidance and introduction to Python programming | Variables, Control statements, Functions, etc. |
| Class 2 | Linear algebra calculations using NumPy | 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 | single-layer perceptron, activation functions, computational graph, automatic differentiation |
| 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 T2SCHOLA.
Evaluation methods and criteria
Based on reports for given assignments.
Related courses
- XCO.T679 ： 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
This exercise is for the doctor course students. When you apply this exercise, it is strongly recommended to take "XCO.T679 Fundamentals of Progressive Artificial Intelligence", "XCO.T677 Fundamentals of Progressive Data Science" and "XCO.T678 Exercises in Fundamentals of Progressive Data Science" of the same quarter of the same year in parallel.
Other
Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using
"fileupload/download" in Google Drive.