MASTER OF SCIENCE IN ARTIFICIAL INTELLIGENCE SYSTEMS
Artificial Intelligence has gained a central position in society and economic systems worldwide. It radically changes our relationship with the significant issues of the contemporary world in health, security, production, transportation, and educational venues. The role of Artificial Intelligence is central to society and will continue to grow. At the same time, implementing computer systems that express such innovation requires a methodological and architectural foundation in software development and database design. The pathway of the Master of Science in Artificial Intelligence Systems incorporates the fundamental features of the changes taking place with a vision attentive to the future evolution of Artificial Intelligence and computer systems development. The major topics addressed include interoperability among information systems, database development (including multimedia), knowledge management, personalized information services, autonomous and multi-agent systems, web-centric services, data warehouses, and machine learning.
The Master of Science in Artificial Intelligence Systems offers two areas of concentration:
Methodologies and Applications with three subareas
Machine Vision
Machine Vision focuses on enabling computers to interpret and make decisions based on visual inputs. This minor delves into the core principles of image processing, pattern recognition, and computer vision. Students will explore techniques for object detection, facial recognition, and scene understanding, as well as the underlying algorithms, such as convolutional neural networks (CNNs) and deep learning frameworks that power these technologies. Practical applications include developing systems for autonomous vehicles, medical image analysis, and industrial inspection. By mastering these concepts, students can create solutions that enhance the capability of machines to understand and interact with their environments visually.
In addition to theoretical knowledge, the Machine Vision minor emphasizes hands-on experience with state-of-the-art tools and software. Students will engage in projects that require them to apply their learning to real-world problems, such as developing vision systems for drones or robotic assistants. This practical approach ensures graduates are well-equipped to tackle challenges in various industries, from healthcare to automotive. By the end of the program, students will have a comprehensive understanding of how to design, implement, and optimize robust and efficient vision systems.
Methodologies
The Methodologies minor is a program dedicated to the systematic approaches and best practices used in developing and deploying artificial intelligence systems. This area covers essential topics such as algorithm design, data science, and statistical methods, providing students with a strong foundation in the theoretical underpinnings of AI. It also includes an in-depth study of machine learning models, including supervised, unsupervised, and reinforcement learning techniques. Students learn to select the appropriate models for specific tasks, tune them for optimal performance, and assess their effectiveness using various metrics and validation techniques. The program also emphasizes hands-on experience, where students gain proficiency in software development practices tailored to AI projects, including agile methodologies, version control, and collaboration tools.
Students will also gain proficiency in software development practices tailored to AI projects, including agile methodologies, version control, and collaboration tools. The curriculum emphasizes the importance of ethical considerations and responsible AI, addressing bias, fairness, and transparency. Through case studies and hands-on projects, students apply these methodologies to real-world scenarios, preparing them to lead AI initiatives in diverse fields such as finance, healthcare, and technology. By the end of the minor, students will be equipped with the skills to design, implement, and manage AI systems that are reliable, ethical, and scalable.
Intelligent Robots
The Intelligent Robots minor is a unique program integrating AI with robotics to create autonomous systems capable of performing complex tasks. This minor covers the fundamentals of robotics, including kinematics, dynamics, and control systems, as well as advanced topics such as sensor integration, path planning, and autonomous navigation. Students will learn how to develop algorithms that enable robots to perceive their surroundings, make decisions, and execute actions in dynamic environments. Key areas of study include robotic vision, motion planning, and human-robot interaction.
Practical experience is a cornerstone of this minor, with students participating in lab sessions and projects that involve designing, building, and programming intelligent robots. These projects may range from developing robotic arms for manufacturing to creating service robots for healthcare or hospitality. The curriculum also explores robotics ethical and societal implications, preparing students to address the challenges and opportunities that arise as robots become more integrated into everyday life. Graduates will have the expertise to innovate in the rapidly evolving field of robotics, contributing to advancements in automation, artificial intelligence, and human-machine collaboration.
Artificial Intelligence and Innovation
The Artificial Intelligence and Innovation minor is designed to empower students with the skills and knowledge needed to drive technological advancements and create innovative solutions using AI. This minor covers the intersection of AI and entrepreneurship, focusing on how artificial intelligence can be harnessed to develop new products, services, and business models. Students will explore various AI technologies, including natural language processing, machine learning, and data analytics, and learn how to apply these technologies to solve real-world problems creatively. Courses will include case studies of successful AI startups and companies, providing insights into the strategies and processes that lead to groundbreaking innovations.
In addition to technical proficiency, the minor emphasizes the importance of creativity, critical thinking, and strategic planning in the innovation process. Students will engage in hands-on projects that require them to conceptualize, design, and implement AI-driven solutions, often working in interdisciplinary teams to simulate real-world scenarios. The curriculum also covers intellectual property rights, funding, and the ethical implications of AI innovations, preparing students to navigate the complex landscape of technology development and commercialization. By the end of the program, students will be equipped with the tools and mindset to lead and inspire AI-driven innovation in various industries, from healthcare and finance to entertainment and beyond.
Educational objectives and methodology
The Master of Science in Artificial Intelligence Systems primary objective is the training of professionals exceptionally competent in data and knowledge modeling, analysis of information flows and decision-making, machine learning, automatic problem solving, or, in general, in advanced techniques and models for the design and development of software and databases. Graduate students will be able to conceive, design, and develop information systems using modern artificial intelligence and distributed software systems development technologies. Students will have the skills necessary to solve problems posed by the growing need for integration and interaction between complex and potentially heterogeneous information systems.
At the end of the master’s program, Graduates should be able to operate autonomously for projects and facilities-attention to both the methodological-scientific training of students and the training of practical and design skills.
Job opportunities
The occupational fields for this study are design, organization, management, and maintenance of complex information systems for organizations that use complex and possibly geographically distributed information systems. Particularly relevant for employment and professional advancement are computer systems for industry, services, health, science, culture, cultural heritage, and public administration. The innovative applications include
artificial intelligence, machine learning, neural networks, soft computing, database, business process management, automatic natural language processing, human-computer interaction, and multimedia databases. Our graduates can work as software architects producing innovative computing solutions and services in research and development centers.
Curricular program
- CORE COURSES (24 CH) these courses provide the foundation for upper-level graduate courses.
COM/521 – Introduction to Robotics
COM/525 – Artificial Intelligence
COM/530 – Signal, Image, and Video
COM/535 – Natural Language Understanding
COM/621 – Human Machine Dialogue
COM/625 – Artificial and Biological Neural Systems
COM/630 – Artificial Intelligence and Innovation
COM/690 – Master Thesis or Capstone Project - CONCENTRATION AREA (9 CH)
To complete the Master’s degree in AIS, students must choose an area of concentration by selecting the most relevant to their career goals. All courses build on what students have learned in the core courses of the Master in Artificial Intelligent Systems program. Students should consult their advisor about scheduling to plan to complete the curriculum.
Methodologies and Applications
- Computer Vision
COM/522 – Computer Vision
COM/523 – Advanced Computer Vision
COM/524 – Trends and Applications of Computer Vision
or
- Methodologies
COM/526 – Advanced Computer Vision
COM/527 – Advanced Topics in Machine Learning and Optimization
COM/528 – Optimization Techniques
or
- Intelligent Robots
COM/531 – Distributed Robot Perception
COM/532 – Optimization Based Robot Control
COM/533 – Robot Planning and its Application
Artificial Intelligence and Innovation
COM/536 – Bio-Inspired Artificial Intelligence
COM/537 – Innovation and Entrepreneurship Basic
COM/538 – Sensing and Radar Technologies - Computer Vision
- GENERAL ELECTIVES: one course chosen by the student and sufficient for it is enough to complete an overall total of 36 credits:
COM/540 – Analysis and Visualization of Complex Networks
COM/541 – Performance Evaluation: Simulation and Modeling
COM/542 – Bioinformatics
COM/543 – Natural Language Technologies
COM/544 – Analysis and Processing of Digital Signals
