KIUS
English


COMPUTER ENGINEERING (MS) (ENGLISH) PROGRAMME
COURSE DESCRIPTION
Name of the Course Unit Code Year Semester In-Class Hours (T+P) Credit ECTS Credit
MACHINE LEARNING CMP513 1 2 3+0 3.0 8.0


General Information
Language of Instruction English
Level of the Course Unit Master's Degree, TYYÇ: Level 7, EQF-LLL: Level 7, QF-EHEA: Second Cycle
Type of the Course Programme Elective
Mode of Delivery of the Course Unit Distance Learning
Work Placement(s) Requirement for the Course Unit No
Coordinator of the Course Unit
Instructor(s) of the Course Unit
Assistant(s) of the Course Unit

Prerequisites and/or co-requisities of the course unit
CATEGORY OF THE COURSE UNIT
Category of the Course Unit Degree of Contribution (%)
Fundamental Course in the field % 50
Course providing specialised skills to the main field % 50
Course providing supportive skills to the main field -
Course providing humanistic, communication and management skills -
Course providing transferable skills -

Objectives and Contents
Objectives of the Course Unit Specialization in Machine Learning in Python and R, The aim is to make accurate predictions and create robust Machine Learning models
Contents of the Course Unit Data analysis for automating analytical model creation with machine learning. Ensuring that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Contribution of the Course Intending to Provide the Professional Education Boyut Küçültme (regresyon) gibi ileri tekniklerin kullanılması ve Güçlendirilmiş Öğrenme, NLP ve Derin Öğrenme gibi konularda bilgi sahibi olma, Mühendislik problemlerinde matematik ve fen bilgisi bilgisi ve anlayışı, Karmaşık mühendislik sistemleri ile ilgili verileri multidisipliner bir bağlamda analiz etme ve yorumlama becerisi. Bilmediğiniz karmaşık mühendislik problemlerini tanımlama, formüle etme ve çözme becerisi, Karmaşık problem çözmede sistem düşüncesini uygulama becerisi, Bağımsız yaşam boyu öğrenme becerisi, daha ileri çalışmaları özerk bir şekilde üstlenme becerisi, Literatür taraması yapabilme, karmaşık teknik konuları takip edebilme ve karmaşık mühendislik problemlerini açık bir şekilde ifade etmek için çeşitli yöntemler kullanabilme becerisi, Karmaşık mühendislik problemlerini tanımlama, formüle etme ve çözme becerisi,

No
 Key Learning Outcomes of the Course Unit
On successful completion of this course unit, students/learners will or will be able to:
1 He will have learned which Machine Learning model to choose for each type of problem
2 Will gain the ability to have knowledge about many Machine Learning models and perform powerful analyses

Learning Activities & Teaching Methods of the Course Unit
Learning Activities & Teaching Methods of the Course Unit

Weekly Course Contents and Study Materials for Preliminary & Further Study
Week Topics (Subjects) Preparatory & Further Activities
1 Data preparation in Python, Python library and datasets.Handling Incomplete Data, Coding Categorical Data, Dividing the dataset into Training set and Test set, Feature Scaling No file found
2 Data preparedness with R. Data Set Description, Importing the Data Set, Handling Missing Data, Encoding Categorical Data, Dividing the Data set into a Training set and a Test set, Feature Scaling, Data Preprocessing Template No file found
3 Simple Linear Regression Intuition - Step 1
The mathematics behind Simple Linear Regression.		 No file found
4 Simple Linear Regression Intuition - Step 2
To Decipher the linear relationship between the independent variable and the dependent variable, to find the most appropriate line by the Ordinary Least Squares method.		 No file found
5 Simple Linear Regression in Python No file found
6 Simple Linear Regression in R No file found
7 Multiple Linear Regression No file found
8 Midterm Exam No file found
9 Polynomial Regression with Python No file found
10 Polynomial Regression with R No file found
11 Support Vector Regression with Python No file found
12 Support Vector Regression with R No file found
13 Decision Tree Decision Regression Intuition with Python No file found
14 Decision Tree Decision Regression Intuition with Python in R No file found

SOURCE MATERIALS & RECOMMENDED READING
1-:”Bayesian Reasoning and Machine Learning”, David Barber
2-“An Introduction to Statistical Learning with Applications in R”, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
HYPERLINK "http://www-stat.stanford.edu/~tibs/"
3- “Understanding Machine Learning: From Theory to Algorithms”, by Shai Shalev-Shwartz and Shai Ben-David

MATERIAL SHARING
Course Notes No file found
Presentations No file found
Homework No file found
Exam Questions & Solutions No file found
Useful Links No file found
Video and Visual Materials No file found
Other No file found
Announcements No file found

CONTRIBUTION OF THE COURSE UNIT TO THE PROGRAMME LEARNING OUTCOMES
KNOWLEDGE
Theoretical
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Learning the theory of the constructs such as Advanced Neural Networks, Evolutionary Optimization, Advanced Machine Learning, Advanced Digital Image Processing, Advanced Cryptography, Data Analytics, Advanced Data Mining, Advanced mathematics for engineering studies X
Factual
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 GAining knowledge on recent research through literature search and project or report submission assignment on the newly published papers on the theoretical knowledge covered X
SKILLS
Cognitive
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Being able to prepare reports based on research X
2 Being able to present personally implemented work X
Practical
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Implementing programming assignments X
PERSONAL & OCCUPATIONAL COMPETENCES IN TERMS OF EACH OF THE FOLLOWING GROUPS
Autonomy & Responsibility
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Self contained reserach X
2 Being able to present personally conducted research and implementation results X
Learning to Learn
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 research X
2 practical programming implementation X
Communication & Social
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Being able to express results of conducted research X
2 Being able to express results of programming implementation X
Occupational and/or Vocational
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Learning methods for data analysis, advanced statistics X
2 Learning programming with new programming languages such as Python X
*Level of Contribution (0-5): Empty-Null (0), 1- Very Low, 2- Low, 3- Medium, 4- High, 5- Very High

No
 Key Learning Outcomes of the Course Unit
On successful completion of this course unit, students/learners will or will be able to: 		PROGRAMME LEARNING OUTCOMES
1 He will have learned which Machine Learning model to choose for each type of problem 1 (5), 2 (5), 3 (5), 4 (5), 5 (5), 6 (5), 7 (5), 8 (5), 9 (5), 10 (5), 11 (5), 12 (5), 13 (5)
2 Will gain the ability to have knowledge about many Machine Learning models and perform powerful analyses1 (5), 2 (5), 3 (5), 4 (5), 5 (5), 6 (5), 7 (5), 8 (5), 9 (5), 10 (5), 11 (5), 12 (5), 13 (5)

Assessment
Assessment & Grading of In-Term Activities Number of
Activities		 Degree of Contribution (%)
Mid-Term Exam 0 -
Computer Based Presentation 0 -
Short Exam 0 -
Presentation of Report 0 -
Homework Assessment 0 -
Oral Exam 0 -
Presentation of Thesis 0 -
Presentation of Document 0 -
Expert Assessment 0 -
Board Exam 0 -
Practice Exam 0 -
Year-End Final Exam 0 -
Internship Exam 0 -
TOTAL 0 %100
Contribution of In-Term Assessments to Overall Grade 0 %50
Contribution of Final Exam to Overall Grade 1 %50
TOTAL 1 %100


WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites Learning Activities
(# of week)		 Duration
(hours, h)		 Workload (h)
Lecture & In-Class Activities 14 3 42
Preliminary & Further Study 14 4 56
Land Surveying 0 0 0
Group Work 0 0 0
Laboratory 0 0 0
Reading 0 0 0
Assignment (Homework) 3 10 30
Project Work 1 20 20
Seminar 0 0 0
Internship 0 0 0
Technical Visit 0 0 0
Web Based Learning 0 0 0
Implementation/Application/Practice 1 20 20
Practice at a workplace 0 0 0
Occupational Activity 0 0 0
Social Activity 0 0 0
Thesis Work 0 0 0
Field Study 0 0 0
Report Writing 1 20 20
Total Workload for Learning & Teaching Activities - - 188
Workload for Assessment Activities
Type of the Assessment Activites # of Assessment Activities
 Duration
(hours, h)		 Workload (h)
Final Exam 1 2 2
Preparation for the Final Exam 1 10 10
Mid-Term Exam 0 0 0
Preparation for the Mid-Term Exam 0 0 0
Short Exam 0 0 0
Preparation for the Short Exam 0 0 0
Total Workload for Assessment Activities - - 12
Total Workload of the Course Unit - - 200
Workload (h) / 25.5 7.8
ECTS Credits allocated for the Course Unit 8.0

EBS : Kıbrıs İlim Üniversitesi Eğitim Öğretim Bilgi Sistemi Kıbrıs İlim Üniversitesi AKTS Bilgi Paketi AKTS Bilgi Paketi ECTS Information Package Avrupa Kredi Transfer Sistemi (AKTS/ECTS), Avrupa Yükseköğretim Alanı (Bologna Süreci) hedeflerini destekleyen iş yükü ve öğrenme çıktılarına dayalı öğrenci/öğrenme merkezli öğretme ve öğrenme yaklaşımı çerçevesinde yükseköğretimde uluslarası saydamlığı arttırmak ve öğrenci hareketliliği ile öğrencilerin yurtdışında gördükleri öğrenimleri kendi ülkelerinde tanınmasını kolaylaştırmak amacıyla Avrupa Komisyonu tarafından 1989 yılında Erasmus Programı (günümüzde Yaşam Boyu Öğrenme Programı) kapsamında geliştirilmiş ve Avrupa ülkeleri tarafından yaygın olarak kabul görmüş bir kredi sistemidir. AKTS, aynı zamanda, yükseköğretim kurumlarına, öğretim programları ve ders içeriklerinin iş yüküne bağlı olarak kolay anlaşılabilir bir yapıda tasarlanması, uygulanması, gözden geçirilmesi, iyileştirilmesi ve bu sayede yükseköğretim programlarının kalitesinin geliştirilmesine ve kalite güvencesine önemli katkı sağlayan bir sistematik yaklaşım sunmaktadır. ETIS : İstanbul Aydın University Education & Training System Cyprus Science University ECTS Information Package ECTS Information Package European Credit Transfer and Accumulation System (ECTS) which was introduced by the European Council in 1989, within the framework of Erasmus, now part of the Life Long Learning Programme, is a student-centered credit system based on the student workload required to achieve the objectives of a programme specified in terms of learning outcomes and competences to be acquired. The implementation of ECTS has, since its introduction, has been found wide acceptance in the higher education systems across the European Countries and become a credit system and an indispensable tool supporting major aims of the Bologna Process and, thus, of European Higher Education Area as it makes teaching and learning in higher education more transparent across Europe and facilitates the recognition of all studies. The system allows for the transfer of learning experiences between different institutions, greater student mobility and more flexible routes to gain degrees. It also offers a systematic approach to curriculum design as well as quality assessment and improvement and, thus, quality assurance.