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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
- Basic science and technology courses
- Instructor(s)
- Defago Xavier
- Class Format
- Lecture / Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- 1a(CS2)
- Course number
- LAS.I122
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- 2024/3/21
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
Students will learn the basics of computer science, specifically the following topics.
・Theoretical studies on computation (computability theory and computational complexity theory).
・More practical programming (using Python libraries).
・Basics of data science.
・Basics of machine learning.
Student learning outcomes
・To understand basics of computability theory and computational complexity theory.
・To be able to write Python programs using basic libraries.
・To understand basics of data science and AI.
Keywords
Computability, Turing machine, computational complexity, P versus NP problem, Python, NumPy, data analysis, machine learning.
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
During the class, programming exercises will be conducted along with lectures. Homework and report assignments to confirm understanding will be given in the class. There are also some quizzes in the class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | What is a computation? (1) | Basics of computability theory: halting problem, Turing machine, universal machine, Church-Turing thesis. |
| Class 2 | What is a computation? (2) | Basics of computational complexity theory: Eulerian path problem, Hamiltonian path problem, traveling salesman problem, P vs NP. |
| Class 3 | More practical Python programming. | Classes and libraries such as NumPy. |
| Class 4 | Foundations of Data Analysis | Learning about summarizing, analyzing, and visualizing data using open data. |
| Class 5 | Basics of Machine Learning | The role and impact of machine learning in problem-solving, with examples of simple unsupervised and supervised learning. |
| Class 6 | Topics on computer science, data science and AI. | Problem setting and programming exercise. |
| Class 7 | Topics on computer science, data science and AI. | Theoretical analysis. |
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)
To be announced in the first class.
Reference books, course materials, etc.
To be announced in the first class.
Assessment criteria and methods
Grades will be based on homework, report assignments and quizzes given in class.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Completion of Computer Science I (LAS.I121) is desirable.
Other
Freshmen must take classes as assigned. Exceptions for class changes may be granted, but changing to Wednesday classes is not allowed. Since Wednesday classes have many freshmen, enrollment for non-freshmen students is not permitted. Non-freshmen students should take a "a" class on Monday or Thursday.
