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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Miyazaki Kei Tomii Norio Okazaki Naoaki Shimosaka Masamichi Sekijima Masakazu Nitta Katsumi Kobayashi Takao Miyake Yoshihiro Ono Isao
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6()
- Group
- -
- Course number
- XCO.T679
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- 2023/9/27
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
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.
Student learning outcomes
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 that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Essential Mathematics for Machine Learning | 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 |
Out-of-Class Study Time (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.
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
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.
