2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Advanced Computer Programming
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Akiyoshi Shioura / Nobutaka Shimizu
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- IEE.A230
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course provides an introduction to algorithms and data structures.
This course uses Python programming language to implement algorithms.
By combining lectures and exercises, the course enables students to understand the fundamentals of programming to solve computational problems.
This lecture is in Japanese.
Course description and aims
At the conclusion of this course students will be able to:
1) learn how to use the Python language.
2) understand basic data structures and algorithms.
3) understand the difference in efficiency of algorithms.
4) implement computing algorithms to solve problems.
Keywords
Python, Programming, Algorithms
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
- Several algorithms and the ability to translate an algorithm into a Python-code.
Class flow
The first two-thids of each class is lecture and the rest for exercises of programming.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | guidance foundation of algorithms recursion exercise | Understanding and implementing basic algorithms and recursive functions using Python. |
| Class 2 | complexity of algorithms definition of graph depth first search, breadth first search exercise | Understanding the concept of algorithm complexity in Big O notation and graph representation in computers (adjacency matrix and adjacency list), and applying algorithms for graph traversal problems based on this understanding. |
| Class 3 | greed algorithms minimum spanning tree exercise | Understanding and applying the framework of greedy algorithms, which is one of the most fundamental methods in combinatorial optimization algorithms, and learning solutions for various problems, starting with the minimum spanning tree problem. |
| Class 4 | shortest path Dijkstra algorithm based on heap exercise | Explaining the shortest path problem on graphs and its solution using Dijkstra's algorithm. The heap data structure, which is used for efficient implementation of Dijkstra's algorithm, will also be explained, and exercises will include problems that apply these concepts. |
| Class 5 | dynamic programming knapsack problem exercise | Explaining the framework of dynamic programming and solutions for various problems, starting with the knapsack problem as its application. |
| Class 6 | matching flow exercise | As fundamental combinatorial optimization problems on graphs, we will explain the maximum matching problem and maximum flow problem, along with algorithms based on alternating paths and the Ford-Fulkerson method. In the exercises, problems that can be solved by applying these algorithms will be presented. |
| Class 7 | introduction to computational complexity exercise | As one of the theoretical foundations for proving that problems cannot be solved efficiently, we will introduce the concepts of reduction and NP-hardness. |
Study advice (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 required.
Reference books, course materials, etc.
Guido van Rossum: Python Tutorial.
Mark Lutz: Learning Python
Evaluation methods and criteria
Learning achievement is evaluated by exercises in every lecture.
Related courses
- IEE.A207 ： Computer Programming (Industrial Engineering and Economics)
Prerequisites
Only undergraduate students of Department of Industrial Engineering and Economics can attend the class.
Students must have completed Computer Programming (Industrial Engineering and Economics) or have equivalent knowledge.
Other
Please bring your PC for in-person classes. Lecture materials will be distributed in ipynb format, so it is essential to have an environment that can open ipynb files, such as creating a Google account (free), opening Google Colaboratory, or installing Jupyter Notebook (details will be explained in the first lecture).
We may also communicate primarily through Slack, so it is highly desirable to enable access to the university-wide Slack. Please refer to the following URL for instructions on how to use the university-wide Slack: