NEW
Wednesday 29 January • 8:30 – 12:45
Course Length: 4 hours
Course Level: Introductory/Intermediate
Instructor: Sos Agaian, The Graduate Center and CSI, City University of New York (CUNY)
The main purpose of this course is to provide the most fundamental knowledge to the attendees so that they can understand what artificial intelligence (AI) is and how to use it in image processing applications. This course enables attendees to:
- Become familiar with AI concepts and applications.
- Understand machine learning and describe the specifics of several prominent machine-learning methods (e.g., SVMs, decision trees, Bayes nets, and artificial neural networks).
- Gain hands-on experience in building, and improving, the performance of AI methods tailoring robotics and vision application.
- Discuss various research solutions for improving current AI algorithms.
Artificial intelligence is a research field that studies how to realize intelligent human behaviors on a computer. The fundamental goal of AI is to make a computer that can learn, plan, and solve problems independently. This course aims to give an overview of some basic AI algorithms and an understanding of the possibilities and limitations of AI. This is an introductory course on artificial intelligence. It emphasizes fast and smart search heuristics, thoughtful ways to represent knowledge, and incisive techniques that support rational decision-making. Application areas will include image processing and robotics.
Intended Audience
Engineers, scientists, students, and managers interested in acquiring a broad understanding of artificial intelligence. Prior familiarity with the basics of image processing is helpful.
Students are expected to have a solid background in the analysis of algorithms, proofs in propositional and first-order logic, discrete mathematics, and elementary probability.
Sos Agaian is currently a distinguished professor of computer science at the City University of New York (CUNY). Prior to this, Agaian was the Peter T. Flawn Professor of electrical and computer engineering at the University of Texas, San Antonio (UTSA). He has been a visiting faculty member at Tufts University in Medford, MA. Currently, at CUNY, Agaian leads the Computational Vision and Learning Laboratory. His research group’s multidisciplinary approach, combining computer science, electrical engineering, behavioral science, neuroscience, and studies in human perception, enables computational devices to see, learn, and understand the physical world as artificially intelligent devices with human-like data processing abilities. Agaian is the recipient of numerous awards including UTSA’s Innovator of the Year Award; and the San Antonio Business Journal’s “The Tech Flash Titans-Top Researcher” Award. Moreover, Agaian established two university research centers: the NSF Center for Simulation Visualization & Real-Time Prediction and the DHS National Center of Academic Excellence in Information Assurance Research. Other honors include IS&T Fellow, IEEE Fellow, SPIE Fellow, and AAAS Fellow. Agaian is an Editorial Board Member for the Journal of Pattern Recognition and Image Analysis, and he is an associate editor for nine journals, including the Journal of Electronic Imaging (SPIE, IS&T), IEEE Transaction on Image Processing; IEEE Transaction on Systems; Man, and Cybernetics; and Journal of Electrical and Computer Engineering (Hindawi Publishing Corporation). Agaian received his MS in mathematics and mechanics (summa cum laude) from the Yerevan State University, Armenia; his PhD in mathematics and physics from the Steklov Institute of Mathematics, Russian Academy of Sciences (RAS); and his doctor of engineering sciences degree from the Institute of Control Systems, RAS.