COURSE INFORMATION Course Title: ARTIFICIAL INTELLIGENCE

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

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Mohammad Ziyad Kagdi mkagdi@epoka.edu.al , 08:40AM to 4:30PM
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Bachelor in Business Informatics (3 years)
Classroom and Meeting Time:	E 312, Friday, 13:30 - 05:00
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	70%
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:	This course is aimed for business informatics students to guide them through the overall concepts of Artificial Intelligence, its types and possible applications. The course further elaborates on strong AI, also knows as Artificial General Intelligence (AGI) or General AI through various philosophical positions and discusses the intricacy of implementing the human consciousness computationally into a machine, also known as Computational Theory of Mind (CTM). Exploring the other side of AI, the course includes concepts like Artificial Life, Swarm Intelligence, Cellular Automata, Genetic Algorithm, Lindenmayer System (L-System) and further into the field of computational neuroscience and machine learning.

BASIC CONCEPTS OF THE COURSE

1	History, introduction and types of Artificial Intelligence
2	Understanding the philosophy of science, demarcation problem and falsifiability of a theory
3	Exploring the philosophical positions to AI with a strong focus on the nature of consciousness
4	Using computer simulations to artificially explore complexity of life
5	Understanding cellular automata its structured patterns
6	P vs NP problem and using evolutionary computations to solve optimisation problems
7	Exploring scale free fractal patterns and biological complexities using in silico modeling of L-system
8	Computational Neuroscience and Artificial Neural Network
9	Implementing machine learning for classifications and predictions
10	Introduction to machine perception and autonomous robotics

COURSE OUTLINE

Week	Topics
1	Introduction to Artificial Intelligence
2	General AI and its Philosophical Positions
3	Machine Consciousness
4	Artificial Life and Cellular Automata
5	P vs NP problems and Evolutionary Computations
6	Swarm Intelligence and Emergence
7	Midterm
8	Lindenmayer System and Complex Systems
9	Introduction to Computational Neuroscience
10	Introduction to Neural Networks
11	Deep Learning
12	Machine Learning
13	Machine Perception and Robotics
14	Revision

Prerequisite(s):	Curious Mindset, Basic to Intermediate Programming Skills
Textbook(s):	Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig
Additional Literature:	Relevant research papers, articles pertaining to Artificial Intelligence
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Intelligent agents and turing Test
2	Exploring philosophy of science and the philosophy of mind
3	Exploring the problem of consciousness and its practical theories
4	Examining systems in natural life using computer simulations
5	Understanding models of complexity using cellular automata
6	Simulating the development of simple organisms using lindenmayer system
7	Solving optimisation problems using darwinian theory of natural selection
8	Understanding the mechanism of the human brain and artificial neural network
9	Implementing machine learning for classifications and predictions
10	Introduction to machine perception and autonomous robotics

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

No	Program Competencies	Cont.
Bachelor in Business Informatics (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	3
2	Apply key theories to practical problems within the global business context.	4
3	Demonstrate ethical, social, and legal responsibilities in organizations.	1
4	Develop an open minded-attitude through continuous learning and team-work.	1
5	Integrate different skills and approaches to be used in decision making and data management.	5
6	Combine computer skills with managerial skills, in the analysis of large amounts of data.	5
7	Provide solutions to complex information technology problems.	3
8	Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world.	3

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	3	10
Midterm Exam(s)	1	15
Project	3	10
Final Exam	1	25
	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	2	32
Hours for off-the-classroom study (Pre-study, practice)	12	4	48
Mid-terms	1	3	3
Assignments	6	6	36
Final examination	1	6	6
Other	0	0	0
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

N/A