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2022　Introduction to Computer Science

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Academic unit or major
Undergraduate major in Mathematical and Computing Science
Instructor(s)
Kashima Ryo  Cong Youyou  Tanaka Keisuke 
Class Format
Lecture / Exercise    (Blended)
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W321)  Thr5-8(W321)  
Group
-
Course number
MCS.T204
Credits
3
Academic year
2022
Offered quarter
1Q
Syllabus updated
2022/3/16
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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.

Student learning outcomes

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 that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

Lecture and programming excercise.

Course schedule/Required learning

  Course schedule Required learning
Class 1 (Lecture and Exercise on Programming 1) Function Definition and Design Recipe Instructed in the class.
Class 2 (Lecture and Exercise on Programming 2) Constants and Auxiliary Functions Instructed in the class.
Class 3 (Lecture on Computer Science 1) logic circuit, boolean algebra Instructed in the class.
Class 4 (Lecture and Exercise on Programming 3) Conditionals Instructed in the class.
Class 5 (Lecture and Exercise on Programming 4) Pattern Matching Instructed in the class.
Class 6 (Lecture on Computer Science 2) machine code Instructed in the class.
Class 7 (Lecture and Exercise on Programming 5) Lists and Recursion Instructed in the class.
Class 8 (Lecture and Exercise on Programming 6) Trees Instructed in the class.
Class 9 (Lecture on Computer Science 3) floating point number, precision in computation Instructed in the class.
Class 10 (Lecture and Exercise on Programming 7) Nested Data Structures Instructed in the class.
Class 11 (Lecture and Exercise on Programming 8) Mutually Recursive Data Structures Instructed in the class.
Class 12 (Lecture on Computer Science 4) programming paradigm, program verification Instructed in the class.
Class 13 (Lecture and Exercise on Programming 9) Higher-order Functions Instructed in the class.
Class 14 (Lecture and Exercise on Programming 10) Polymorphic Functions Instructed in the class.
Class 15 (Lecture on Computer Science 5) computability Instructed in the class.
Class 16 (Lecture and Exercise on Programming 11) Generative Recursion Instructed in the class.
Class 17 (Lecture and Exercise on Programming 12) Accumulators Instructed in the class.
Class 18 (Lecture on Computer Science 6) computational complexity Instructed in the class.
Class 19 (Lecture and Exercise on Programming 13) N Puzzle Instructed in the class.
Class 20 (Lecture and Exercise on Programming 14) Summary Instructed in the class.
Class 21 (Lecture on Computer Science 7) ethics of science Instructed in the class.

Out-of-Class Study Time (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/)

Assessment criteria and methods

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 (i.e., required knowledge, skills, courses, etc.)

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.

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