Computational Thinking

Course Objective

Knowledge and understanding:
at the end of the course, students will be familiar with basic knowledge of some solution strategies (e.g., guest and check, modeling, formulas,) and algorithms (search algorithms, sorting algorithms, and graph algorithms) to solve problems. Students will also have knowledge of creating algorithms.

Applying knowledge and understanding:
students will be able to implement basic solutions strategies, search algorithms, sorting algorithms, and graph algorithms.

Making judgements:
students will be able to choose the right solution strategy and apply that to solve problems.

Communication skills:
students will be able to write a project report and give an oral presentation of their project to peers.

Learning skills:
students will be trained in analyzing problems, translating algorithms to flowchart, and writing pseudocodes to implement algorithms.

Course Content

There are various strategies to solve everyday problems. Often a problem can be solved in different ways and there is not always a "best way". However, sometimes one way is more efficient than the other, or you find one approach easier or more pleasant than the other. In this course, you will be acquainted with different solution strategies (such as modeling, formulas, guess and check) and algorithms (such as search algorithms, sorting algorithms and graph algorithms) to solve problems. You will learn to solve problems by reasoning and by using knowledge from other disciplines. In the practical sessions, you will resolve various problems using the different solution strategies and algorithms that have been discussed in the course. Since there are many ways to solve a problem, you will also start thinking about developing algorithms yourself. In this course, we encourage your problem solving and algorithmic thinking, as well as your creative and innovative skills. At the end of the course, you will work together with some other students in a group on a project. You will conclude the project with a short presentation.

Additional Information Teaching Methods

Method of Assessment

The final grade is based on the individual practicum assignments (45%), group project assignment (40%), and two supervised quizzes (15% together) on campus.

For all three parts separately, the average grade should be at least a 5.5 to pass the course.

During the project presentations, questions are asked that are considered during the project's grading. This means that grades within a group may vary from person to person.

You CANNOT redo the practical assignments or the project if you have not passed them or if you have not completed them. If the average grade of the practical assignments or the project is less than 5.5, you will fail the course and must retake the course next year.

Literature

Syllabus and video lectures/clips

Additional Information Target Audience

Bachelor Artificial Intelligence - year 1

Additional Information

This course revolves around self-study with video lectures in combination with class sessions (once per week) for deepening understanding and practical sessions (once per week).

Use of generative AI is only permitted for orientation and brainstorming

In this course, generative AI (GenAI), such as ChatGPT or similar tools, may only be

used for orientation and brainstorming, for example to explore ideas or clarify

questions. GenAI may not be used to generate content that becomes part of the

final submission. The final product must be based entirely on the student's own

understanding, analysis, and wording. Incorrect of unapproved use of GenAI is

considered fraud. If established, the submitted work or assessment result will be

declared invalid, and the Examination Board may impose further measures.

Custom Course Registration

For this course, please enroll for the module, via VUweb. The faculty will enroll you for the other teaching methods.

Students will be placed in a practicum group in Canvas to participate in the practical sessions.