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School of Environment and Society
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- Graduate major in Technology and Innovation Management
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- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Miyazaki Kei Tomii Norio Yanagisawa Keisuke Okazaki Naoaki Shimosaka Masamichi Sekijima Masakazu Nitta Katsumi Kobayashi Takao Miyake Yoshihiro Ono Isao
- Class Format
- Exercise (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8()
- Group
- -
- Course number
- XCO.T680
- 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 skills that is essential for creating applications of artificial intelligence, implementing basic concepts and theories as a computer program.
Student learning outcomes
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 that will be developed
| ✔ 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". Exercises are conducted via Zoom.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 |
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 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 (i.e., required knowledge, skills, courses, etc.)
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.
