13E054VI - Artificial Intelligence
| Course specification | ||||
|---|---|---|---|---|
| Course title | Artificial Intelligence | |||
| Acronym | 13E054VI | |||
| Study programme | Electrical Engineering and Computing | |||
| Module | Signals and Systems | |||
| Type of study | bachelor academic studies | |||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 6.0 | Status | elective | |
| Condition | none | |||
| The goal | This course provides a broad technical introduction and a survey of core concepts of artificial intelligence, including search, planning and reasoning under uncertainty. The goal is to provide students with both theoretical and practical tools to model, design and test artificial intelligence systems, with practical implementation of agents in Python and Matlab. | |||
| The outcome | Students will be able to independently analyze the environment the agent is to operate in and the goals it should attain, to choose adequate artificial intelligence algorithms for the problem at hand and to design, practically implement and test these algorithms using Python and/or Matlab. | |||
| Contents | ||||
| URL to the subject page | https://automatika.etf.bg.edu.rs/sr/13e054vi | |||
| URL to lectures | https://teams.microsoft.com/l/team/19%3ApW-wwekT7v04rrcDlNnkCRa95alWpY-WTdksXWb92Jg1%40thread.tacv2/conversations?groupId=cb2fcd3a-b234-49ae-be8f-04cf0acd013b&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba | |||
| Contents of lectures | History, definition and overview of basic concepts in artificial intelligence. Search and planning: classical, local and adversarial search, constraint satisfaction problem. Probabilistic reasoning: Bayes nets, exact and Monte Carlo inference. Temporal models: hidden Markov models, dynamic Bayes nets, Kalman and particle filter. | |||
| Contents of exercises | Detailed solutions to problems which demonstrate the mechanics and important aspects of algorithms covered in lectures. Interactive demonstrations and analysis of practical implementations of these algorithms applied to illustrative problems. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 3 | 1 | 1 | ||
| Methods of teaching | oral lectures, blackboard exercises, computer demonstrations | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | 0 | Test paper | 60 | |
| Practical lessons | 0 | Oral examination | 0 | |
| Projects | ||||
| Colloquia | 20 | |||
| Seminars | 20 | |||