Moodle @ Department of Computer Science UCY

The course teaches intermediate and advanced programming concepts, techniques and tools through a language that compiles to machine code. The course familiarizes the students with advanced programming constructs utilized for handling memory and files. Advanced topics in compilation, debugging, documentation and optimization of software. Methodological aspects in developing large-scale system software that addresses complex problems. Basic commands for programmers in the UNIX operating system. i) Introduction to C for Programmers: types x86/x64, loops, selections, expressions, arrays, functions, IO, basic program organization, ii) Advanced C programming constructs: program anatomy and processes, memory and addresses (pointers, pointers and arrays, strings and examples), structures, unions and enumerations. Linear and non-linear programming data structures (dynamic memory allocation, lists, queues, doubly-linked lists, trees, applications and examples). iii) Advanced Compilation Topics and Tools: preprocessor directives, compiling multiple files with makefiles, static (.a) and dynamic (.so) linking of object files (.o), error handling (assert.h), static and dynamic code analysis (valgrind and gprof). iv) low-level programming (binary operators and examples, binary files and hexdump). v) Basic commands for programmers in the UNIX operating system: file system, redirection and pipes, permissions and basic filters.

The main objective of this undergraduate course is to provide an in-depth understanding of concepts related to the design and utilization of a database management system. Students will get a deeper understanding by implementing these concepts in a commercial database management system. The course is organized in four parts: i) Introduction and Conceptual Modeling using the ER Model, ii) Relational Model and Relational Algebra, iii) Structured Query Language III, and iv) Database Design Theory and Methodology

Outline: Introduction: Databases and Database Users, Database System Concepts and Architecture, Data Modeling Using the Entity-Relationship (ER) Model, The Enhanced Entity-Relationship (EER) Model, The Relational Data Model and Relational Database Constraints, Relational Algebra, Relational Database Design by ERand EER-to-Relational Mapping, SQL-99: Schema Definition, Constraints, Queries, and Views, Introduction to SQL Programming Techniques, Functional Dependencies and Normalization for Relational Databases, Relational Database Design Algorithms and Further Dependencies, Practical Database Design Methodology. 

Course Website: https://www.cs.ucy.ac.cy/courses/EPL342/

The purpose of the MAI611 AI Fundamentals course is to introduce students to the fundamental principles and techniques that underline software systems that exhibit “intelligent” behavior.

Upon completion of this course, students will have acquired a good understanding of symbolic Artificial Intelligence, the problems that it addresses and the fundamental solution methods that it uses. More specifically the students will know the main knowledge representation techniques and reasoning methods that underlie artificial intelligence problem solving and be able to develop simple solvers for artificial intelligence systems.

The course introduces the fundamental principles and methods used in Artificial Intelligence to solve problems, with a special focus on the search in the state space, action planning, knowledge representation and reasoning, constraint satisfaction, intelligent agents and on the methods for dealing with uncertain knowledge. The course content includes the following thematic units:

·       Introduction to Artificial Intelligence: Turing test for machine intelligence, historical perspective, symbol processing, algorithms and heuristics, main application fields, introduction to knowledge-based systems and architectural organization.

·       Problem Solving in AI: Representation problem, navigation mechanisms (blind search, heuristic guidance), classification and synthesis problems, frame problem. Games. Constraint satisfaction problems. Search methods, meta heuristics, solving through decomposition, constraint relaxation, branch-and-bound techniques. Linear planning.

·       Intelligent Agents and Multiagent Systems: Autonomous/rational agents, abstract and concrete architectures, communication, interaction, and communication between agents.

·       Knowledge Representation and reasoning: Distinction between data, information and knowledge, expertise (types of knowledge), desirable practical and theoretical properties, descriptive/declarative versus procedural representation. Predicate logic. Conjunctive Normal Form and resolution-refutation. Horn clauses and negation as failure. Representing Terminological Knowledge: semantic networks, frames and inheritance. Uncertainty and probabilistic reasoning.

·       Rule-based systems: Production rules, control structure and rule chaining, forward chaining and RETE, backward chaining, goal-driven reasoning and explanations, meta-rules, strategic explanations. Limitations of rule-based systems.

·       Expert Systems Technology: Basic forms of reasoning (deduction, abduction, induction). First and second generation of expert systems (deep knowledge-based systems, multi-modelling, strategic explanations, collaborating specialists, generic tasks architecture).

·       Knowledge engineering methodologies: Developing and modelling expertise, total task investigation methods, basic principles, and multiple models of CommonKADS methodology.

·       Case-based reasoning as an alternative paradigm to model-based reasoning supporting incremental learning.

·       Intelligent Data Analysis: Data mining and data abstraction.

·       AI and Explanations: The significance of explanations in AI, some theories of explanation that have influenced AI, tracing the history of explanations in symbolic AI (rule-based, abductive, causal, case-based, strategic, explanations), the resurgence of interest in explanation in connectionist AI (XAI) – opening the ‘black box’.

This interdisciplinary course covers a variety of topics within the intersection of Artificial Intelligence and Human-Computer Interaction, with an emphasis on incorporating human factors in intelligent interactive systems’ design. Specifically, it covers: i) theoretical foundations of intelligent user interfaces and the importance of human factors in such contexts; ii) state-of-the-art processes and techniques for implementing intelligent interactive systems; and iii) research and practice on how intelligent user interfaces can be applied in various application domains.

https://dmsl.cs.ucy.ac.cy/

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