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This self-paced course is the second in a series of courses on foundational algorithms of AI. This course is an introduction into key search algorithms developed in the field of AI. The thorough theoretical treatment of these algorithms is complemented with numerous examples that illustrate the search mechanisms implemented by a search algorithm
Who is this course for?
The course is intended for anyone new to the field of AI that requires a general introduction
What will you learn?
By the end of this course you will be able to understand and explain systematic, heuristic, local and stochastic search algorithms
Topics overview: Systematic search (concepts, strategies and algorithms), heuristic search algorithms (GBFS, A*, IDA*, MM) and local and stochastic searches (searching very large search spaces, simulated annealing)
How much time do you need to invest? Completing the course should take approximately four (4) weeks at three (3) hours per week. Hence, 12 hours in total.
Course Certificate: A course certificate will be generated on the successful completion of the final quiz
Course Developers: Dr Sophia Saller, Annika Engel and Prof Dr Jana Koehler in cooperation with KI Campus. (This course has been adapted and modified in accordance with its open license)
Course Contact: fairforward@giz.de
For technical questions, please reach out to the atingi support team via the help-desk on the top right or to atingi@giz.de
Course License: CC BY-SA 4.0
Learning pathway: This course is part of a learning pathway that includes the following courses:
- Foundations of Artificial Intelligence I
- Introduction to Machine Learning Problem Framing
- Machine Learning: Data Preparation and Feature Engineering
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