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)	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 Computer 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 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:	Machine Learning (ML) studies how computers can be made to behave intelligently. In this course we will cover theoretical and practical approaches to ML, with topics to include search, logic, knowledge representation, uncertainty, and different aspects of the performance of ML techniques.

BASIC CONCEPTS OF THE COURSE

1	Linear Algebra
2	Statistics
3	Programming

COURSE OUTLINE

Week	Topics
1	Introduction to Machine Learning and Overview
2	Data Preprocessing (Data cleaning, normalization, data encoding, data splitting etc.)
3	Linear Regression, Gradient descent, cost function, MSE etc.
4	Logistic Regression and Classification (Accuracy, Precision, Recall, F1-score, confusion matrix)
5	Decision Trees and Random Forests
6	Support Vector Machines (SVM)
7	Unsupervised Learning: Clustering and SOM
8	Midterm Exam
9	Neural Networks and Deep Neural Networks
10	Recurrent Neural Networks (RNNs) and Time Series, LSTM
11	CNN, Autoencoders, and the other machine learning models (ADAM, ROCKET, RESNET etc.)
12	Hybrid and Ensemble Models
13	Final Project and Presentations-1
14	Final Project and Presentations-2

Prerequisite(s):	Introductory linear algebra and statistics
Textbook(s):	Karlik Bekir, Lecturer notes of Machine Learning, Epoka University, 2023
Additional Literature:	Pattern Recognition and Machine Learning by Christopher M. Bishop
Laboratory Work:	2 hours
Computer Usage:	Basic Python (or C++) programming skills,
Others:	No

COURSE LEARNING OUTCOMES

1	Students will be able to Understand and Explain Key Machine Learning Concepts: Define the types of machine learning (supervised, unsupervised, reinforcement learning) and the types of problems they solve. Articulate the fundamental concepts of training, testing, validation, overfitting, underfitting, and cross-validation.
2	Students will be able to Preprocess and Prepare Data for Machine Learning Models: Clean, preprocess, and manipulate real-world datasets to ensure they are ready for machine learning applications (handling missing values, normalizing features, encoding categorical variables).
3	Students will be able to use both Implement and Evaluate Supervised and Unsupervised Learning Models.
4	Students will be able to work Independently and Collaboratively on Machine Learning Projects (Develop, implement, and present a comprehensive machine learning project, demonstrating the ability to apply all aspects of the course to a real-world dataset or problem. Collaborate effectively with peers in group assignments and class discussions, sharing knowledge and insights on machine learning topics.)

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

No	Program Competencies	Cont.
Bachelor in Computer 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.	5
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	20
Presentation	1	30
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