This set of 3 Master-level courses (5+5+5(+2)=15(+2) credits) is the complete and complementary coverage of the modern Artificial Intelligence (AI) theory and technologies. It is taught in English by Prof. Vagan Terziyan (who has already 40 years of experience in AI). AI content is divided according to 3 major and popular approaches: Top-Down AI; Bottom-Up AI; and Autonomic AI, each of which is taught as a separate 5-credit course as follows:

- ITKS5440: Semantic Web and Linked Data;

- TIES4910: Deep Learning for Cognitive Computing. Theory;

- TIES4530: Collective Intelligence and Agent Technology.

Each of the courses can be studied either separately as a stand-alone course or one can choose any combination of these courses as a package. The courses are self-contained (include all needed background content) and adapted to a wide audience of potential students (from a variety of programs) so that THERE IS NO NEED FOR ANY PRE-REQUESITY. Courses can be studied REMOTELY as all the needed material and instructions are available within the courses’ web pages online. Course assessment and grading are based on written assignments, with NO EXAM.


Brief description of the courses is as follows:


I. Semantic Web and Linked Data. /5 credits, Autumn Semester/ (The course concerns the so called Top-Down (“Symbolic”) approach to AI, when AI is designed on the basis of data, information and knowledge, which is represented in a standardized way and suitable for automated machine processing and understanding, sharing, data and application integration, reuse, reasoning and inference, etc. This includes various aspects of Knowledge Representation and Reasoning, Data Tagging, Metadata Creation, Semantic Web, Linked Data, Ontology (i.e. Web-based shared knowledge) Engineering, etc.

Course Web page:


II. Deep Learning for Cognitive Computing. Theory. /5 credits, Autumn Semester/ (The course concerns the so called Bottom-Up (“Statistical”) approach to AI, when AI can be trained on the basis of available data. This includes various aspects of Machine Learning (Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning, Adversarial Learning, Deep Learning, etc.). Cognitive Computing part is represented by a variety of deep neural network architectures (including recurrent, convolutional, adversarial, etc.) suitable for processing and translating natural language texts, speech-to-text and text-to speech transformations, speech recognition, tone (emotional and other) analysis on the basis of texts, capturing cognitive profiles of people, recognizing and tagging patterns from images and videos, generating new texts, handwritings, speech, images (including art), etc.

Course Web page:


III. Collective Intelligence and Agent Technology. /5 - 7 credits, Spring Semester/ (The course concerns the so called Autonomic (“Self-Managed”) approach to AI, when AI is represented by autonomous intelligent agents (i.e., software robots) capable to fully manage themselves (having self-trained models of own objectives, beliefs, desires, intentions, plans, values, consciousness to some extent, etc., and being capable to communicate, negotiate, collaborate, replicate, etc., among each other within collaborative or competitive environments). The topic is very challenging and interesting due to the popular assumption that such autonomous agents can one day become superintelligent and take majority of jobs from humans. Therefore we believe that the best and safest way for a human in such a context is to study (be professional in) this topic and even to drive it further towards AI benefits (at least keep-on-eye on it).

Course Web page: