| Week |
Subject |
Related Preparation |
| 1) |
Introduction to Artificial Intelligence
- Overview of AI and its applications
- Historical development of AI
- AI ethics and societal impact
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 2) |
Problem-Solving and Search Algorithms
- Problem formulation and state-space representation
- Uninformed search algorithms: BFS, DFS
- Heuristic search algorithms: A* Search
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 3) |
Adversarial Search and Game Playing
- Minimax algorithm
- Alpha-Beta Pruning
- Evaluation functions for game-playing agents
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 4) |
Knowledge Representation and Logic
- Propositional and Predicate Logic
- Knowledge representation using First-Order Logic
- Resolution and Inference in logic
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 5) |
Uncertainty and Probabilistic Reasoning
- Bayesian networks
- Probabilistic inference using Bayes' theorem
- Decision networks and utility theory
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 6) |
Machine Learning Fundamentals
- Supervised learning and unsupervised learning
- Regression and classification algorithms
- Evaluation metrics for ML models
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 7) |
Neural Networks and Deep Learning
- Neural network architectures
- Backpropagation algorithm
- Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 8) |
Midterm |
|
| 9) |
Reinforcement Learning
- Markov Decision Processes (MDPs)
- Q-learning and Value Iteration
- Policy Gradient methods
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 10) |
Natural Language Processing (NLP)
- Text preprocessing and tokenization
- Sentiment analysis and text classification
- Sequence-to-sequence models for machine translation
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 11) |
Computer Vision and Image Processing
- Image feature extraction
- Object detection and recognition
- Image segmentation and image generation
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 12) |
AI in Robotics
- Robot kinematics and motion planning
- Localization and Simultaneous Localization and Mapping (SLAM)
- Reinforcement learning for robotic control
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 13) |
AI Ethics and Future Trends
- Ethical considerations in AI development and deployment
- AI in industry and research
- Emerging trends in Artificial Intelligence
|
Artificial Intelligence: A Modern Approach (AIMA), 4th Edition, Stuart J. Russell, Peter Norvig, Prentice Hall, 2020.
http://aima.cs.berkeley.edu/ |
| 14) |
Final Exam
This course content covers a wide range of AI topics, from problem-solving and search algorithms to machine learning, deep learning, and AI in robotics. Each week focuses on specific AI subdomains, allowing students to gain a comprehensive understanding of the field. Hands-on projects and practical exercises are incorporated throughout the course to provide students with practical experience in AI applications.
|
|
| |
Program Outcomes |
Level of Contribution |
| 1) |
Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply. |
4 |
| 2) |
Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines. |
4 |
| 3) |
Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution. |
4 |
| 4) |
Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process. |
4 |
| 5) |
Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints. |
3 |
| 6) |
Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process. |
2 |
| 7) |
Ability to use a foreign language (English) at least at the level of
European Language Portfolio B2 in verbal and written communication. |
3 |
| 8) |
Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility. |
4 |
| 9) |
Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values. |
3 |
| 10) |
Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary. |
5 |