Computational Thinking
Foundations of computational thinking: solution strategies, algorithms (search, sorting, graph), and problem-solving for AI. One of the most accessible and beginner-friendly courses in the programme.
Practicum sessions are not mandatory to attend, but your practicum group IS your project group (worth 40%). Sign up for a group on Canvas — you cannot switch later.
Learning objectives
Knowledge and understanding: at the end of the course, students will be familiar with basic knowledge of some solution strategies (e.g., guess 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.
Computational Thinking is one of the friendliest introductions to the AI bachelor programme. Over just five weeks, you'll learn different solution strategies (modeling, formulas, guess and check), fundamental algorithms (linear search, binary search, bubble sort, merge sort, quicksort), and graph algorithms (Dijkstra's, Prim's, Kruskal's). The course uses a flipped-classroom format: you watch short video lectures at home and then work through real-world cases and exercises during the live sessions. No prior programming experience is needed — Python basics are introduced gently alongside the algorithmic concepts. Weekly practicum assignments let you apply what you've learned immediately, and the course concludes with a fun group project where you design, implement, and present an algorithm for a real-world scenario. The supervised quizzes are short multiple-choice and true/false tests that closely follow the syllabus, so if you keep up with the weekly material you'll do great.
Assessment
The final grade is based on the individual practicum assignments (45%), group project assignment (40%), and two supervised quizzes (15% together) on campus (15%). 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.
Teaching methods
(video) lectures, practical sessions, project, presentations, self-study
Literature
Syllabus and video lectures/clips