COURSE INFORMATION Course Title: MACHINE LEARNING

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 380	C	6	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. Florenc Skuka fskuka@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Florenc Skuka fskuka@epoka.edu.al , Friday 10:00 - 12:00
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 Business Informatics (3 years)
Classroom and Meeting Time:	PC_LAB_2, Thursday: 15:45 - 20:45
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 introduces students to machine learning algorithms, data preprocessing techniques, and practical applications. Students will gain a hands-on understanding of how to build machine learning models, evaluate their performance, and apply them to real-world datasets. By the end of the course, students will be able to design, implement, and evaluate machine learning models using popular Python libraries such as scikit-learn and TensorFlow.
Course Objectives:	This course on Machine Learning will explain how to build systems that learn and adapt using real-world applications (such as robotics and brain wave signal understanding). Some of the topics to be covered include reinforcement learning, neural networks, genetic algorithms and genetic programming, parametric learning (density estimation), clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.

BASIC CONCEPTS OF THE COURSE

COURSE OUTLINE

Week	Topics
1	Introduction to Machine Learning
2	Linear Regression with one Variable
3	Multivariate Linear Regression.
4	Regularizes, Learning curves
5	Optimization
6	Classification: Linear classification
7	Support Vector Machines
8	Kernels
9	Neural Network: Representation
10	Neural Network: Learning, Backpropagation
11	Convolutional Neural Network
12	Unsupervised Learning: Clustering
13	Anomaly Detection
14	Project Presentations

Prerequisite(s):	Linear Algebra, Calculus
Textbook(s):	T. Mitchell, Machine Learning, McGraw-Hill
Additional Literature:	C.Bishop, Pattern Recognition and Machine Learning, Springer
Laboratory Work:
Computer Usage:
Others:	No

COURSE LEARNING OUTCOMES

1	Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
2	Have an understanding of the strengths and weaknesses of many popular machine learning approaches.
3	Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
4	Be able to design and implement various machine learning algorithms in a range of real-world applications.

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.	4
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	5	4
Midterm Exam(s)	1	20
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	5	80
Hours for off-the-classroom study (Pre-study, practice)	12	4	48
Mid-terms			0
Assignments	5	3	15
Final examination	1	20	20
Other	1	24.5	24.5
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			6

CONCLUDING REMARKS BY THE COURSE LECTURER