COURSE INFORMATION Course Title: ARTIFICIAL INTELLIGENCE

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 352	C	5	2	2	0	3	6

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Dr. Halit Vural hvural@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Halit Vural hvural@epoka.edu.al , Tue: 10:50 ~ 11:20
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Stela Lila slila@epoka.edu.al
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Bachelor in Software Engineering (3 years)
Classroom and Meeting Time:	Tue: 8:40 ~ 10:30 | 11:40 ~ 13:30
Teaching Assistant(s) and Office Hours:	NA
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	Yes
Course Description:	This course provides an overview of methods, history, and impact of AI. It covers problem solving, heuristic search, planning, game playing, reasoning with propositional and predicate logic, reasoning under uncertainty, machine learning, applications (natural language processing, vision, robotics, as time permits). Students will solve a variety of AI problems using Python. Includes a discussion of the role of AI technology in society.
Course Objectives:	Artificial intelligence studies how computers can be made to behave intelligently. In this course we will cover theoretical and practical approaches to AI, with topics to include search, logic, knowledge representation, uncertainty, and different aspects of the performance of AI techniques.

BASIC CONCEPTS OF THE COURSE

1	AI Fundamentals: Explain different types of learning in AI and their real-world applications.
2	Search & Problem Solving: Implement basic search algorithms to solve puzzles and pathfinding tasks.
3	Learning from Data: Train and evaluate ML models (Decision Trees, SVMs) for simple datasets.
4	Neural Networks: Build and test basic neural networks (MLP, CNN) for recognition tasks.
5	Reinforcement Learning: Simulate simple agents that learn through trial and error.
6	AI & Society: Recognize ethical issues (bias, privacy) and discuss their impact.

COURSE OUTLINE

Week	Topics
1	Introduction to AI (definitions, history, learning types)
2	Problem Solving by Search (BFS, DFS, UCS)
3	Local Search & Heuristics (Hill Climbing, A*, Bounded Memory Search)
4	Rule-Based Systems (Logical Agents)
5	Search-Based Planning + Bayesian Networks (Intro)
6	Statistical Learning: SVM & Decision Trees
7	Midterm
8	Neural Networks 1 (Perceptron, MLP)
9	Neural Networks 2 (Backpropagation, Hopfield, SOM)
10	Advanced Neural Architectures (CNN, RNN)
11	Reinforcement Learning (Q-learning, Policy Iteration)
12	Other Learning Methods + Intro to Deep Learning Frameworks
13	Artificial Intelligence Ethics & Generative AI
14	Project Presentations

Prerequisite(s):	Analysis of Algorithms
Textbook(s):	Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition.
Additional Literature:	-
Laboratory Work:	2 hours
Computer Usage:	Python, C++
Others:	No

COURSE LEARNING OUTCOMES

1	Students understand and explain the core principles of AI.
2	Students can implement classical search algorithms and basic problem solvers.
3	Students can apply machine learning methods (decision trees, SVM, Bayesian models).
4	Students can build and train simple neural networks.
5	Students are able to discuss the ethical and societal impact of AI.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Bachelor in Software Engineering (3 years) Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	5
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	4
8	Ability for effective oral and official communication skills in foreign language.	5
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	5
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	5
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	2	5
Midterm Exam(s)	1	30
Project	1	20
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	4	64
Hours for off-the-classroom study (Pre-study, practice)	16	2	32
Mid-terms	1	15	15
Assignments	1	15	15
Final examination	1	20	20
Other	1	4	4
Total Work Load:			150
Total Work Load/25(h):			6
ECTS Credit of the Course:			6

CONCLUDING REMARKS BY THE COURSE LECTURER