Artificial Intelligence and Machine Learning
Course code
IFI6242.DT
old course code
Course title in Estonian
Tehisintellekt ja masinõpe
Course title in English
Artificial Intelligence and Machine Learning
ECTS credits
6.0
Assessment form
Examination
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Provide knowledge of machine learning methods and algorithms used in the field of artificial intelligence and the practical skills to use them to create software.
Brief description of the course
Linear regression. Adjusting data and interpreting results. Grouping data, describing groups. Estimation of group membership, yield and precision. Decision trees. Operating principles of neural networks. Machine learning applications in linguistics. Two-player minimax algorithm. Common graph algorithms and their applications. The possibilities of Prolog for building decision systems.
Learning outcomes in the course
Upon completing the course the student:
- understands how to construct a linear regression model and assess its reliability;
- understands how to cluster data objects, measure model yield and accuracy;
- understands examples of the mechanisms of operation underlying artificial intelligence algorithms in the natural world, understands how to simulate them in the context of their own system and how to find approximate results;
- can use breadth-first, depth-first and other common graph algorithms to solve problems, convert input and output data into a form to be used in the final application;
- be able to use finite automata and generative grammars to build or test an application.
- understands how to construct a linear regression model and assess its reliability;
- understands how to cluster data objects, measure model yield and accuracy;
- understands examples of the mechanisms of operation underlying artificial intelligence algorithms in the natural world, understands how to simulate them in the context of their own system and how to find approximate results;
- can use breadth-first, depth-first and other common graph algorithms to solve problems, convert input and output data into a form to be used in the final application;
- be able to use finite automata and generative grammars to build or test an application.
Teacher
Jaagup Kippar
Prerequisite course 1
Study programmes containing that course