TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Professor Murat Paşa Uysal
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Acquire the necessary knowledge and skills to use information systems and artificial intelligence applications in accordance with the strategic objectives of enterprises, 2) Know subject areas of Big Data, Data Mining, Machine Learning (ML), Deep Learning, differences from each other, application purposes and methods, 3) Know and apply: mathematical, statistical components and bases of ML, 4) Know ML general application steps, to be able to use in solving different types of problems, 5) Apply ML general application steps within the framework of Project Management Discipline, 6) Know ML Software and Application Development Environments and use in solving different problems, 7) Know and apply Supervised Machine Learning Models / Algorithms, 8) Know and apply Unsupervised Machine Learning Models / Algorithms, 9) Know and apply Image Processing Learning models / algorithms, 10) Know and apply Text and Natural Language Processing Learning models / algorithms, 11) Carry out a ML Term Project for the solution of a business problem, perform the required ML analysis, design and implementation processes to produce required project outputs
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	The aim of this course is to teach various techniques that enable learning from data by using basic and advanced concepts related to machine learning. This course, in which different techniques and algorithms are compared and applications are made, basically answers the question of how to learn from past experiences.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to course; Introduction to Artificial Intelligence and Basic Concepts, Application Areas, Effects on Personal, Social and Business World; Main Topics: Big Data, Data Mining, Machine Learning (ML) Basic Components, ML's Mathematical and Statistical Basics, 2nd Week The Purposes of Using Information Systems in Businesses, Their Effects on Organization, The Forces Affecting Competition Model, Business Value Chain and Network Models; Introducing Orange ML and Data Mining Software Environment 3rd Week Introducing Orange ML and Data Mining Software Environment; General Application Steps of Machine Learning and Its Projects: Using Logistic Regression and Random Forest Learning Models (Algorithms) 4th Week Statistical Basics of Machine Learning: Basic and Core Statistical Concepts; Statistics Applications in Orange ML and Data Mining Software Environment 5th Week Mathemetical Basics of Machine Learning: Matrices and Matrix Operations; Use of Matrix Operations in Machine Learning 6th Week Supervised Machine Learning Models: Linear and Multiple Linear Regression Models 7th Week Supervised Machine Learning Models: Decision Trees Learning Model 8th Week Midterm Exam 9th Week Supervised Machine Learning Models: Random Forest Learning Model 10th Week Supervised / Unsupervised Machine Learning Models 11th Week Supervised / Unsupervised Machine Learning Models 12th Week Image Processing Learning Models (Logistic Regression etc.) 13th Week Text and Natural Language Processing Learning Models 14th Week Evaluation of General Review and Term Projects
RECOMENDED OR REQUIRED READING	Andreas C. Müller, Sarah Guido (Author) (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media. Oliver Theobald (2018). Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch). Independent Publisher. Mueller, J.P., Massaron, L.(2016). Machine Learning For Dummies, John Wiley & Sons, Inc., USA. Oliver Theobald (2018). Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch). Pramod Singh, (2019). Machine Learning with PySpark, APress Media, NY, USA. Puneet Mathur, (2019). Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. , APress Media, NY, USA. Ella Hassanien (2019). Machine Learning Paradigms: Theory and Application. Springer. Andrzej Wodecki ( 2019).Artificial Intelligence in Value Creation. Palgrave Macmillan. Wolfgang Ertel (2017). Introduction to Artificial Intelligence, Springer-Verlag London Limited. UK. Stuart Russell and Peter Norvig. (2010).Artificial Intelligence: A Modern Approach. 3rd edition. USA.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Project-Supported Traditional lnstructional Method
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 40 Project 1 20 Other 1 10 Total(%) 70 Contribution of In-term Studies to Overall Grade(%) 70 Contribution of Final Examination to Overall Grade(%) 30 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 13 2 26 Preparation for Quiz Individual or group work 14 1 14 Preparation for Final exam 1 22 22 Course hours 13 2 26 Preparation for Midterm exam 1 14 14 Laboratory (including preparation) 13 2 26 Final exam 1 1,5 1,5 Homework Presentation (including preperation) 1 3 3 Project 1 3 3 Term Project Research 1 8 8 Report writing 1 8 8 Total Workload 151,5 Total Workload / 30 5,05 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No