MACHINE LEARNING PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
BIL415	MACHINE LEARNING	5	3	3	6

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Elective
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. OĞUZHAN ÖZTAŞ
Instructor(s) of the Course Unit	Assist.Prof. ERKAN USLU-Assist.Prof. OĞUZHAN TAŞ
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	To give students basic ideas and intuitions behind machine learning theory, artificial neural networks algorithms, statistical learning methods as well as theoretical and practical understanding of how, why and when they are used.
Contents of the Course Unit:	Supervised Learning; Bayes Rule; Naive Bayes; Decision Trees; Linear Discriminant; Multilayered Perceptron; Support Vector Machine; Unsupervised Learning; Maximum Expectation; k-means; Gauss Mixture Model; Learning with Award-Punishment

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Decides which machine learning method is suitable to given.
Knows difference between regression and classification algorithms and uses them in suitable problems.
Knows and applies hierarchical clustering algorithms and neighborhood based algorithms.
Knows and applies supervised and unsupervised dimensionality reduction methods.
Knows and explains parametric and nonparametric methods.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Introduction to Machine Learning	-
2	-	Introduction to python and jupyter notebook	-
3	-	Naïve bayes	-
4	-	Naïve bayes-implementation and more examples	-
5	-	Decision Tree	-
6	-	Descision Tree- model evaluation	-
7	-	Confusion matrix-Overfitting-K nearest Neighbor	-
8	-	MID-TERM EXAM	-
9	-	Linear and logistic regression	-
10	-	Neural network and Deep learning	-
11	-	Reinforcement learning	-
12	-	Clustering	-
13	-	Project presentations	-
14	-	Project presentations	-
15	-	Project presentations	-
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Introduction to Machine Learning, 2e, Ethem Alpaydın, The MIT Press, 2010
Machine Learning, Tom Mitchell, McGraw Hill, 1997
Pattern Classification, 2e, R. O. Duda, P. E. Hart, D. G. Stork, Wiley Interscience, 2001

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	30
Homework Assessment	1	10
Short Exam	1	10
Final Exam	1	50
Mid-Term Exam	1	30
Practice	1	15
Short Exam	1	5
Final Exam	1	50
TOTAL	8	200

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Explains the fundamental engineering concepts of computer science and relates them to the groundwork of computer science.					4

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Uses theoretical and practical knowledge coming from mathematics, probability, statistics and various other branches of life sciences, to find solutions to engineering problems.					4

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines the components and the underlying process of a system and designs an appropriate computational model under reasonable constraints.						5
2	Designs a computer-aided conceptual model with modern techniques.						5

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines, detects and analyzes the areas of computer science applications and develops appropriate solutions.					4
2	Identifies, models and solves computer engineering problems by applying appropriate analytical methods.						5
3	Determines and uses the necessary information technologies in an efficient way for engineering applications.					4

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Possess the responsibility and ability to design and conduct experiments for engineering problems by collecting, analyzing and interpreting data.					4
2	Possess the ability to conduct effective individual study.					4
3	Takes responsibility as a team work and contributes in an effective way.				3

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Monitors the developments in the field of information technologies by means of internet and related journals and possess the required knowledge for the management, control, development and security of information technologies.						5
2	Develops positive attitude towards lifelong learning.						5

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Communicates effectively by oral and/or written form and uses at least one foreign language.			2
2	Possess sufficient consciousness about the issues of project management, practical applications and also environmental protection, worker's health and security.				3

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Possess professional and ethical responsibility and willingness to share it.		1
2	Possess sufficient consciousness about the universality of engineering solutions and applications and be well aware of the importance of innovation.			2

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	3	42
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	2	10	20
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	2	10	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	0	0	0
Final Exam	1	2	2
Preparation for the Final Exam	1	20	20
Mid-Term Exam	1	2	2
Preparation for the Mid-Term Exam	1	3	3
Short Exam	2	1	2
Preparation for the Short Exam	2	2	4
TOTAL	40	0	157