Fundamentals Of Artificial Intelligence
Public syllabus for 2025-2026
Academic overview
Teaching team
Learning time distribution
| Total | ||||||
|---|---|---|---|---|---|---|
| Curriculum | Lecture | Practice | Total Weekly | Lecture | Practice | |
| 42 | 28 | 14 | 3 | 2 | 1 | |
| Exam hours | ||||||
| 6 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 108 | 23 | 23 | 50 | 6 | 0 | |
| Overall | ||||||
| 150 |
Learning outcomes
Knowledge
- Introduction in Artificial Intelligence
Skills
- Knowledge of main topics of Artificial Intelligence and specific applications
Responsibility
- Work in teams and individual projects
Online platform
Course content
| Content | Methods | Obs |
|---|---|---|
| C1. Introduction into artificial intelligence. Intelligent agents. | Lecture, exemplification, demonstration | 2h, [2] Chapters 1, 2 M.Marin |
| C2. Solving problems by searching. Uninformed and informed search strategies. Heuristic functions. | Lecture, exemplification, demonstration | 2h, [2] Chapter 3 M.Marin |
| C3. Strategies for constraint satisfaction problems. Constraint propagation. Search strategies (intelligent backtracking, constraint learning) | Lecture, exemplification, demonstration | 2h, [2] Chapter 5 M.Marin |
| C4-5.Logical agents and inference in first order logic | Lecture, exemplification, demonstration | 2h [2] Chapters 8, 9 M.Marin |
| C6. Strategies for optimization problems. Deterministic strategies (hill-climbing). Stochastic strategies (simulated annealing, evolutionary algorithms) | Lecture, exemplification, demonstration | 2h, [2] Chapter 4 D. Zaharie |
| C7. Probabilistic reasoning. Probability theory reminder. Naïve Bayes models. Bayesian networks. | Lecture, exemplification, demonstration | 2h, [2] Chapter 12,13 D. Zaharie |
| C8. Supervised learning models (regression and classification) and gradient descent. Neural networks and the backpropagation algorithm. | Lecture, exemplification, demonstration | 2h D. Onchis |
| C9. Introduction to unsupervised learning (clustering, deep unsupervised learning). | Lecture, exemplification, demonstration | 2h D. Onchis |
| C10. Markov decision processes, evaluating policies and finding the optimal value with value iteration. Reinforcement learning, Q-learning and SARSA | Lecture, exemplification, demonstration | 2h D. Onchis |
| C11. Philosophical Foundations of Artificial Intelligence | Lecture, exemplification, demonstration | 2h (online) M. Milojevic |
| C12. AI and Society. Applications in media & fake news, social networks, medicine, autonomous transport systems | Lecture, exemplification, demonstration | 2h (online) M. Milojevic |
| C13. Ethics of AI | Lecture, exemplification, demonstration | 2h (online) M. Milojevic |
| C14. Recap. Projects presentations | Lecture, exemplification, demonstration | 2h (online) All professors |
Course bibliography
[1] Stuart Russell, „The history and future of AI”, Oxford Review of Economic Policy 37 (3), 509-520, 2021 [2] Stuart Russell, Peter Norvig, „Artificial intelligence: a modern approach”, global edition 4th, Foundations 19, 23, 2021 [3] Tom Mitchell, „Machine Learning”, McGraw-Hill, 1997 [4] Christopher Manning, Hinrich Schuetze, „Foundations of Statistical Natural Language Processing”,; MIT Press, 2009https://cs50.harvard.edu/ai/2020/, https://cs50.harvard.edu/ai/2020/weeks/0/https://stanford-cs221.github.io/spring2022/, https://stanford-cs221.github.io/spring2022/modules/ http://ai.stanford.edu/~nilsson/mlbook.html
Seminar content
| Content | Methods | Obs |
|---|---|---|
| L1. Implementation of search-based algorithms | Dialog, Problem posing, Implementation | 2h M.Marin |
| L2. CSP solvers & Logic | Dialog, Problem posing, Implementation | 2h M. Marin |
| L3. Strategies for optimization problems | Dialog, Problem posing, Implementation | 2h D..Zaharie |
| L4. Probabilistic programming | Dialog, Problem posing, Implementation | 2h D.Zaharie |
| L5. Implementation from scratch of basic neural networks for classification and regression. | Dialog, Problem posing, Implementation | 2h D. Onchis |
| L6. Applications in media & fake news, social networks, medicine, autonomous transport systems. | Dialog, Problem posing, Implementation | 2h D. Onchis |
| L7. Reading AI and ethics. | Dialog, Problem posing, Implementation | 2h (online) M. Milojevic |
| Bibliography: [1] Stuart Russell, Peter Norvig, „Artificial intelligence: a modern approach”, global edition 4th, Foundations 19, 23, 2021 [2] Tom Mitchell, „Machine Learning”, McGraw-Hill, 1997 [3] François Chollet, „Deep Learning with Python”, November 2017, ISBN 9781617294433 [4] Lab materials: https://darianonchis.wordpress.com/ or Google Classroom [5] http://scikit-learn.org/stable/ [6] https://www.tensorflow.org/ [7] https://keras.io/ [8] https://colab.research.google.com [9] https://www.kaggle.com/ [10] OR-tools https://developers.google.com/optimization/cp/cp_solver [11] https://pypi.org/project/CSP-Solver/ [12] https://www.pymc.io/projects/docs/en/stable/learn.html |
Seminar bibliography
The course follows the overall structure of S. Russel and P. Norvig text book on AI which is world-wide adopted by universities. The contents of the course are related but not significantly overlapping with the Machine Learning and Data Mining course. The course is intended to follow the needs of the IT companies active in the field.
Corroboration
(none)
AI tools guidance
Evaluation and delivery
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Performance standards
Knowledge of at least three basic Artificial Intelligence algorithms. Realization of a project. Correct usage of the presented Artificial Intelligence software packages.
Additional info
(none)