1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF TECHNOLOGY
  3. BIOMEDICAL ENGINEERING DEPARTMENT
  4. 185 BIOMEDICAL ENGINEERING
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
BMM 429MACHINE LEARNING3 + 05th Semester4

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective The main objective of this course is to teach students the concept of machine learning and different learning methods. At the end of the course, the students will gain skills for selecting and applying an appropriate learning methods for real-life problems and analyzing the performance of the method in terms of error and complexity.
Course Content Introduction to machine learning, supervised learning, multi-variable models and regression, model order and generalization properties, k-means clustering, decision trees, Bayes decision theory, artificial neural networks, support vector machines, dimensionality reduction and principal component analysis.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1"1. He/She knows the fundamental concepts about machine learning 2. Differentiate between machine learning methods 3. Understand the idea of how to select the most appropriate parameters when implementing a machine learning method 4. Coding and analysing applicable machine learning methods on a given data"

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12PO 13PO 14
LO 001              
Sub Total              
Contribution00000000000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Mid-terms12020
Final examination12222
Internet Searching/ Library Study12020
Total Work Load

ECTS Credit of the Course





104

4
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Spring1EŞREF BOĞAR
Details 2022-2023 Fall1METE OKAN ERDOĞAN
Details 2021-2022 Fall1METE OKAN ERDOĞAN
Details 2019-2020 Spring1EŞREF BOĞAR


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
BMM 429 MACHINE LEARNING 3 + 0 1 Turkish 2023-2024 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Asts. Prof. Dr. EŞREF BOĞAR ebogar@pau.edu.tr TEK A0002 %60
Goals The main objective of this course is to teach students the concept of machine learning and different learning methods. At the end of the course, the students will gain skills for selecting and applying an appropriate learning methods for real-life problems and analyzing the performance of the method in terms of error and complexity.
Content Introduction to machine learning, supervised learning, multi-variable models and regression, model order and generalization properties, k-means clustering, decision trees, Bayes decision theory, artificial neural networks, support vector machines, dimensionality reduction and principal component analysis.
Topics
WeeksTopics
1 Introduction to machine learning
2 Supervised learning
3 Multi-variable models and regression
4 Multi-variable models and regression
5 Artificial neural networks
6 Artificial neural networks
7 Artificial neural networks
8 Clustering algorithms
9 Clustering algorithms
10 Support vector machines
11 Support vector machines
12 Support vector machines
13 Decision trees
14 Dimensionality reduction and principal component analysis
Materials
Materials are not specified.
Resources
ResourcesResources Language
T. Mitchell, "Machine Learning", McGraw-Hill, 1997.English
C. M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2007.English
S. Haykin, "Neural Networks and Learning Machines", Prentice Hall, 2008.English
R. O. Duda, Pattern Classification, Wiley-Interscience, 2000.English
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes