Programme Description

This M.Sc. programme brings a deep insight into the latest techniques used in building intelligent systems and AI-based applications. It offers to the enrolled students in-depth theoretical and practical knowledge and a cutting-edge approach on:

  • artificial intelligence and intelligent applications
  • knowledge representation and automated reasoning
  • computer vision
  • intelligent agents and their application
  • neural networks and deep learning
  • advanced machine learning techniques
  • natural language understanding
  • real-world applications of AI
Careers for M.Sc. graduates
  • Our AI Master of Science graduates can easily find employment in many different areas, such as: robotics, self-driving cars, intelligent games development, intelligent search, image and video understanding (faces, people, activities), automated translation, music and entertainment, chat bots, medical diagnostics, business forecasting, banking, insurance, telecommunications, consumer electronics, smart cities, and many other, as most of tomorrow applications will be AI-based.
  • Graduates can find employment in multi-national companies, small companies or start-ups, governmental agencies, or follow a career in research or academia.
Programme Structure and Content

ECTS: 120, Duration: 4 semesters, Type: Research

The degree requirements for students in the Artificial Intelligence programme consist of core courses, options, and supervised research culminating in a Master Thesis. The Master’s Degree Programme is designed to be completed in 4 semesters, with the last semester dedicated mainly to research and the fulfillment of the Master Thesis.


Sem I

  • Knowledge Representation and Reasoning
  • Computer Vision
  • Data Mining
  • 1 Elective

Sem II

  • Multi-agent Systems
  • Natural Language Processing
  • Symbolic and Statistical Learning
  • 1 Elective


  • Self-organizing Systems
  • Neural Networks
  • Advanced Topics in Artificial Intelligence (robot control, self-driving cars, advanced planning techniques, ambient intelligence)
  • 1 Elective

Sem IV

  • Research activities and development of the Master Thesis
Examples of research topics
  • Interaction with social robots,
  • Navigation using mobile robots,
  • Natural language interaction with humanoid robots,
  • Human activity recognition,
  • Discourse analysis and recommendation,
  • Language emergence in multi-agent systems,
  • Hierarchical learning, Lifelong learning,
  • Object recognition and manipulation based on machine learning algorithms,
  • Using generative models to improve deep reinforcement learning,
  • Question answering using neural networks and string kernels,
  • Efficient exploration in deep reinforcement learning with intrinsic motivation,
  • People tracking for autonomous vehicles,
  • Visual scene understanding for autonomous vehicles,
  • Intelligent ChatBots,
  • Text-based sentiment analysis using deep learning,
  • Lightweight cross-platform agent system for IoT,
  • Conversational agent modeling a specific personality,
  • Model-based deep reinforcement learning,
  • Autonomous driving through reinforcement learning,
  • Clustering and semantic recommendation of relevant documents,
  • Face and emotion recognition,
  • Swarm formation and control.
Entry Requirements

Applicants should hold a Bachelor degree in Computer Science, Computer Engineering, Information Technology, Systems Engineering, Informatics, Mathematics or related areas. Applicants are required to pass an admission examination. More details on entry requirements are to be found here.

Foreign student applications will be considered based on the curriculum content of their Bachelor Degree and academic performance.

Programme director

University Politehnica of Bucharest
Faculty of Automatic Control and Computers
AI-MAS Laboratory

For any administrative issues and queries, please go to:
Master of Science Studies | ACS – For further details on the master programme;
Contact Us | ACS – For details on contacts that can answer your questions regarding the admission process (section: M.Sc. Admission).

Information on previous programmes