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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed7-8(W241)
- Group
- -
- Course number
- XCO.T282
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/4/4
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course gives basic theories, methods, and algorithms of data science, data engineering, and AI to students those who have completed the course "Basics and Applications of Data Science and Artificial Intelligence I" in the first quarter. The curriculum is designed so that it provides an intermediate-level course study of data science and AI between literacy- and expert-level ones. The course would enable students to understand theories and methods deeply and achieve practical skills in problem solving through a variety of examples and exercises.
Student learning outcomes
visualization methods.
2) Understand roles of data engineering, representation methods of various data on a computer, and data acquisition/processing/accumulation techniques.
3) Understand history of AI, its technical background, AI ethics, machine learning and learning algorithms, neural networks and deep learning algorithms, and apply AI technology to problem solving.
Keywords
Estimation, statistical test, regression analysis, least squares method, unsupervised leaning, supervised leaning, reinforcement learning, DNN, CNN, RNN, recognition, prediction
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
To check students’ understanding, students are assigned a quiz at the end of every class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Fundamentals of data science, part 1 | Understand basics of data science, especially focusing on methods of estimation and hypothesis testing. |
| Class 2 | Fundamentals of data science, part 2 | Understand data analysis methods such as regression analysis, multiple-regression analysis, and least squares methods. |
| Class 3 | Machine learning, part 1 | Understand basics of machine learning algorithms by applying them to practical problems such as classification and clustering. |
| Class 4 | Machine learning, part 2 | Understand basics of machine learning algorithms in which topics include supervised learning, cross validation, overfitting, and reinforcement learning. |
| Class 5 | Neural networks and deep learning, part 1 | Understand principles of artificial neural networks and their training algorithms in which topics includes perceptron, multilayer perceptron, and back propagation algorithm. |
| Class 6 | Neural networks and deep learning, part 2 | Understand structures and mechanisms of useful neural networks such as deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). |
| Class 7 | AI applications | Understand roles of AI technology in our daily life by looking at a wide variety of examples to which AI technology has been successfully applied. |
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. Lecture materials will be given in the class.
Reference books, course materials, etc.
Lecture materials will be found on T2SCHOLA in advance and shared in Zoom lecture.
Assessment criteria and methods
Grading is based on quizzes and term-end report.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
- LAS.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- LAS.M105 ： Calculus II
- LAS.M106 ： Linear Algebra II
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students must have successfully completed Information Literacy I, Information Literacy II, Computer Science I, and Computer Science II.
