2021 Faculty Courses School of Computing Undergraduate major in Mathematical and Computing Science
Introduction to Computer Science
- Academic unit or major
- Undergraduate major in Mathematical and Computing Science
- Instructor(s)
- Ryo Kashima / Youyou Cong / Keisuke Tanaka
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon (W641) / 5-8 Thu (W641)
- Class
- -
- Course Code
- MCS.T204
- Number of credits
- 210
- Course offered
- 2021
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course consists of "Lecture and Exercise on Programming" and "Lecture on Computer Science".
Lecture/Exercise on Programming fosters skills to analyze problems and design programs based on program design recipes.
Lecture on Computer Science gives an overview of computer science, including logic circuit, machine code, low-level and high-level programming languages, and theory of computability and complexity. Ethical issues in science are also discussed.
This course is the starting point of all the studies in the Department of Mathematical and Computing Science.
Course description and aims
At the end of this course, students will acquire basic knowledge and skills in programming and computer science.
Keywords
programming, computer science, ethics of science.
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lecture and programming excercise.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | (Lecture on Programming 1) Function Definition and Design Recipe | Instructed in the class. |
| Class 2 | (Exercise on Programming 1) Function Definition and Design Recipe | Instructed in the class. |
| Class 3 | (Lecture on Computer Science 1) logic circuit, boolean algebra | Instructed in the class. |
| Class 4 | (Lecture on Programming 2) Conditionals and Pattern Matching | Instructed in the class. |
| Class 5 | (Exercise on Programming 2) Conditionals and Pattern Matching | Instructed in the class. |
| Class 6 | (Lecture on Computer Science 2) machine code | Instructed in the class. |
| Class 7 | (Lecture on Programming 3) Lists and Recursion | Instructed in the class. |
| Class 8 | (Exercise on Programming 3) Lists and Recursion | Instructed in the class. |
| Class 9 | (Lecture on Computer Science 3) floating point number, precision in computation | Instructed in the class. |
| Class 10 | (Lecture on Programming 4) Complex Data Structures | Instructed in the class. |
| Class 11 | (Exercise on Programming 4) Complex Data Structures | Instructed in the class. |
| Class 12 | (Lecture on Computer Science 4) programming paradigm, program verification | Instructed in the class. |
| Class 13 | (Lecture on Programming 5) Higher-Order and Polymorphic Functions | Instructed in the class. |
| Class 14 | (Exercise on Programming 5) Higher-Order and Polymorphic Functions | Instructed in the class. |
| Class 15 | (Lecture on Computer Science 5) computability | Instructed in the class. |
| Class 16 | (Lecture on Programming 6) Generative Recursion and Accumulators | Instructed in the class. |
| Class 17 | (Exercise on Programming 6) Generative Recursion and Accumulators | Instructed in the class. |
| Class 18 | (Lecture on Computer Science 6) computational complexity | Instructed in the class. |
| Class 19 | (Lecture on Programming 7) Summary and Application | Instructed in the class. |
| Class 20 | (Exercise on Programming 7) Summary and Application | Instructed in the class. |
| Class 21 | (Lecture on Computer Science 7) ethics of science | Instructed in the class. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend a certain length of time outside of class on preparation and review (including for assignments), as specified by the Tokyo Institute of Technology Rules on Undergraduate Learning (東京工業大学学修規程) and the Tokyo Institute of Technology Rules on Graduate Learning (東京工業大学大学院学修規程), for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Instructed in the class.
Reference books, course materials, etc.
How to Design Programs (https://htdp.org/)
Evaluation methods and criteria
Students' scores are determined by the achievements in Exercise on Programming and Lecture on Computer Science.
Their weights are 67% and 33%, respectively.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
Prerequisites
Only the students in Department of Mathematical and Computing Science can take this course.
Students must have successfully completed Information Literacy I and II (LAS.I111, LAS.I112) or have equivalent knowledge.