Overview, Definition, and Objectives
The Artificial Intelligence (AI) program aims to educate students capable of designing, analyzing, and implementing intelligent systems. The curriculum is founded upon a deep understanding of the technical and mathematical foundations of AI, encompassing key subjects such as mathematics and statistics, machine learning, natural language processing, computer vision, data mining, and other core areas of artificial intelligence.
This program emphasizes academic excellence and research-oriented learning. Through modern pedagogical models—particularly Learning by Doing—students develop their applied and research skills by engaging in real-world projects under the supervision of faculty members. As a result, graduates of this program gain not only theoretical and technical mastery, but also the ability to analyze, design, and implement intelligent systems in both industrial and research contexts.
In addition to specialized technical training, students are introduced to courses such as Philosophy of Artificial Intelligence and Ethics of Artificial Intelligence, fostering an integrated, interdisciplinary, and philosophical perspective. Emphasis on humanistic and spiritual dimensions, as well as on the principles of justice and transparency alongside technological advancement, forms a core strategic foundation of the program.
Overall, this specialization nurtures students who are technically proficient in AI while also advancing in research and innovation with a responsible, ethical, and quality-oriented approach.
Prerequisite Courses
| Course Title | Credits |
|---|---|
| Linear Algebra | 3 |
| Engineering Probability and Statistics | 3 |
| Design of Algorithms | 3 |
| Artificial Intelligence | 3 |
| Signals and Systems | 3 |
Core Courses
| Course Title | Credits |
|---|---|
| Machine Learning | 3 |
| Deep Learning | 3 |
| Foundations of Statistical Learning | 3 |
| Deep Reinforcement Learning | 3 |
| Trustworthy Machine Learning | 3 |
| Convex Optimization | 3 |
| Meta-Heuristic Optimization | 3 |
| Autonomous Mobile Robots | 3 |
Elective Courses
| Course Title | Credits |
|---|---|
| Advanced Deep Learning | 3 |
| Statistical Machine Learning | 3 |
| Multi-Agent Systems | 3 |
| Probabilistic Graphical Models | 3 |
| Machine Learning Theory | 3 |
| Big Data Analysis | 3 |
| Complex Networks Analysis | 3 |
| Fuzzy Methods and Systems | 3 |
| Stochastic Processes | 3 |
| Digital Signal Processing | 3 |
| Machine Learning Systems Engineering | 3 |
| Computer Vision | 3 |
| 3D Computer Vision | 3 |
| Image Processing | 3 |
| Information Hiding | 3 |
| Natural Language Processing | 3 |
| Advanced Natural Language Processing | 3 |
| Intelligent Information Retrieval | 3 |
| Speech Processing | 3 |
| Speech and Speaker Recognition | 3 |
| Text-to-Speech Conversion | 3 |
| Robot Localization and Navigation | 3 |
| Introduction to Neuroscience | 3 |
| Computational Cognitive Science | 3 |
| Planning in Artificial Intelligence | 3 |
| Cognitive Robotics | 3 |
| Human-Robot Interaction | 3 |
| Special Topics in Artificial Intelligence 1 | 3 |
| Special Topics in Artificial Intelligence 2 | 3 |