COURSE INFORMATION Course Title: ARTIFICIAL INTELLIGENCE

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 352	B	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)	Prof.Dr. Bekir Karlik bkarlik@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Prof.Dr. Bekir Karlik bkarlik@epoka.edu.al
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:
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:
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	Learning algorithms
2	Statistical learning models
3	Neural Networks
4	Introduction to deep Learning

COURSE OUTLINE

Week	Topics
1	Introduction to AI (Learning and learning types, basic definitions)
2	Solving problems by searching
3	Local search algorithms and Heuristics and Bounded Memory Search
4	Rule-Based Systems and Search-Based Planning
5	Bayesian Networks, Statistical Learning
6	The other statistical learning (SVM)
7	Model Decision Tree Processes
8	Neural Networks -1 (Perceptron Learning, MLP etc.)
9	Midterm
10	Neural Networks -2 (Backpropagation, Hopfield, SOM, etc.)
11	Neural Networks -3 (Reinforcement Learning, RNN, CNN etc.)
12	Other learning methods and Intro to Deep Learning
13	Artificial Intelligence Ethics, Generative AI
14	Project Presentations

Prerequisite(s):	Analysis of Algorithms
Textbook(s):	Karlik Bekir, Lecturer notes of Intro to AI, Epoka University
Additional Literature:	Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd edition
Laboratory Work:	2 hours
Computer Usage:	Python, C++
Others:	No

COURSE LEARNING OUTCOMES

1	Student will be able to design and implement a knowledge-based artificial intelligence agent that can address a complex task using the methods discussed in the course.
2	Student will be able to use this agent to reflect on the process of human cognition.
3	Student will be able to use both these practices to address practical problems in multiple domains.

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

NA