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)	Dr. Florenc Skuka fskuka@epoka.edu.al
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
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
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:	75%
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	Introduction to Robotics and Subsumption Architecture using a Braitenberg vehicle
3	Understanding the philosophy of science, demarcation problem and falsifiability of a theory
4	Exploring the philosophical positions to AI with a strong focus on the nature of consciousness
5	Using computer simulations to artificially explore complexity of life
6	Understanding cellular automata and its structured patterns
7	P vs NP problem and using evolutionary computations to solve optimisation problems
8	Exploring collective intelligence of decentralised and self-organised systems using Swarm intelligence
9	Gaining insight about functioning of biologically plausible neurons using computational simulations
10	Trashcan project demonstrating the reactive robotic architecture

COURSE OUTLINE

Week	Topics
1	Introduction to Artificial Intelligence: Understanding the nature and the difference between intelligence and artificial intelligence, the history of AI, types, applications and the singularity hypothesis
2	AI and Robotics: Discussing how an artificial agent can interact with its environment and act upon it using its actuators.
3	Philosophy of Science: Indentifying the key difference between science and pseudoscience by understanding the criteria of verifiability and falsifiability
4	General AI and its Philosophical Positions: Exploring the concept of the Mind-body problem in the philosophy of mind to understand the positions of physicalism and substance-dualism
5	The Hard Problem of Consciousness: Understanding why and how humans and other organisms have qualia, phenomenal consciousness, or subjective experience and if it is possible in principle to imitate it in a machine.
6	Artificial Life and Cellular Automata: Artificial Life is a study of examining systems related to natural life, its processes and evolution using computer simulations.
7	The P versus NP problem and Evolutionary Computation: The P vs NP problem asks whether every problem whose solution can be quickly verified in a reasonable amount of time (in polynomial time) can also be quickly solved. Evolutionary Computation (EC) is a family of algorithms with a character of metaheuristic, inspired by the Darwinian theory of natural selection (Biological evolution). EC is widely studied in the field of Artificial Intelligence and Soft computing.
8	Midterm
9	Swarm Intelligence: Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems consist typically of a population of simple agents interacting locally with one another and with their environment.
10	Computational Neuroscience: Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics.
11	Artificial Neural Network: ANN consists of connected artificial neurons, which loosely model the neurons in the brain where each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the activation function.
12	Chaos, Nonlinearity, Complex Systems and Emergence: Complex systems are systems whose behavior is intrinsically difficult to model due to the dependencies, competitions, relationships, or other types of interactions between their parts or between a given system and its environment.
13	Robotics Trashcan Demonstration: Students will work in groups to design a robot that would achieve goal-directed behaviour using subsumption architecture in simulated artificial arena.
14	Robotics Trashcan Demonstration: Students will work in groups to design a robot that would achieve goal-directed behaviour using subsumption architecture in simulated artificial arena.

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

COURSE LEARNING OUTCOMES

1	Intelligence and Artificial Intelligence
2	Intelligent agents and Robotics
3	Exploring philosophy of science and the philosophy of mind
4	Exploring the problem of consciousness and its practical theories
5	Examining systems in natural life using computer simulations
6	Understanding models of complexity using cellular automata
7	Solving optimisation problems using darwinian theory of natural selection
8	Understanding physiology and dynamics of the nervous system
9	Studying Chaos, Complexity and Non-linearity
10	Trashcan Robotics Demonstration

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	2	10
Midterm Exam(s)	0	0
Presentation	1	15
Project	2	20
Laboratory	1	25
Final Exam	0	0
	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)	11	3	33
Mid-terms	0	0	0
Assignments	6	10	60
Final examination	0	0	0
Other		0	0
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

This course expects students to have curious and investigative mindset as well as an intermediate level of programming skills.