URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2AMU10&collegejaar=2025&taal=enDescription
Artificial Intelligence (AI) is an expanding field with goals related to studying, understanding, designing and developing intelligent solutions which can replace or enhance human intelligence. This course introduces students to the foundations of artificial intelligence, from its initial developments to the current state of the art. It gives an understanding and vision of what is and what is not achievable with past and existing AI techniques. The course covers historical facts and AI developments, AI limitations and complexity, common knowledge representation techniques in AI, reasoning, inferences and decision making.Objectives
After taking this course students should be able to:- Name and understand the foundations of AI and historical facts and developments in the field.
- Understand capabilities, theoretical limitations and open directions in AI.
- Represent some knowledge in AI systems.
- Understand, derive and calculate using basic principles of automated inferences.
- Understand and compare different AI approaches
- Design and implement simple AI systems using different AI approaches.
- Choose appropriate AI models and algorithms for different problems.
- Design, execute, and report on experiments to compare the efficacy of different AI techniques.
Assumed knowledge/prerequisites for this course are:
* Linear Algebra (for example as taught in 2DRR00 or 2WF20)
* Statistics (for example as taught in 2DI90, JBM010, 2WS20, or 2WS30; certainly: notions of experimental design)
* Probability Theory (for example as taught in 2DI90, JBM010, 2DL70; certainly: laws of probability, multivariate probability, marginal and conditional probability, Bayes’s rule, independence, random variables, probability mass functions, probability density functions, cumulative distribution functions)
* Logic & Set Theory (for example as taught in 2IT60 or 2IHT10; certainly: set & logical operations, first order logic, logic notation, inference rules such as modus ponens, Boolean algebra)
* Algorithms & Data Structures (for example as taught in 2IL50 or 2IHA10; certainly: graphs, trees, iteration, recursion, search, basic optimization)
* Programming and Programming Experience (Python; certainly: algorithm implementation, object-orientation, modularity, documenting/commenting, debugging, scripting, data processing, data analysis, plot generation)