To be able to apply and evaluate machine learning techniques.
Course Content
This course contains; Elements of machine learning,Regression,Basics of classification,Bayesian classifier,Logistic regression,Support vector machines,Neural networks,Convolutional neural networks,Decision trees,Ensemble methods,Feature selection,Principal component analysis,Clustering,Model evaluation.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Applies regression techniques
12, 14, 16, 6, 9
A, E
Evaluates classification techniques
12, 14, 16, 6, 9
A, E
Applies unsupervised machine learning techniques
12, 14, 16, 6, 9
A, E
Applies feature selection / analysis techniques
12, 14, 16, 6, 9
A, E
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Elements of machine learning
2
Regression
3
Basics of classification
4
Bayesian classifier
5
Logistic regression
6
Support vector machines
7
Neural networks
8
Convolutional neural networks
9
Decision trees
10
Ensemble methods
11
Feature selection
12
Principal component analysis
13
Clustering
14
Model evaluation
Resources
Bishop, “Pattern Recognition and Machine Learning,” Springer, (1st
edition)
Duda, Hart, and Stork, “Pattern Classification,” Wiley-Interscience, (2nd
edition)
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
X
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
X
7
An ability to communicate effectively
X
8
A recognition of the need for, and an ability to engage in life-long learning
X
9
An understanding of professional and ethical responsibility
X
10
A knowledge of contemporary issues
X
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
30
Rate of Final Exam to Success
70
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
0
0
0
Term Project
0
0
0
Presentation of Project / Seminar
0
0
0
Quiz
0
0
0
Midterm Exam
1
24
24
General Exam
1
24
24
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
90
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(90/30)
3
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
INTRODUCTION to MACHINE LEARNING
-
Fall Semester
3+0
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Name of Lecturer(s)
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Assistant(s)
Aim
To be able to apply and evaluate machine learning techniques.
Course Content
This course contains; Elements of machine learning,Regression,Basics of classification,Bayesian classifier,Logistic regression,Support vector machines,Neural networks,Convolutional neural networks,Decision trees,Ensemble methods,Feature selection,Principal component analysis,Clustering,Model evaluation.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Applies regression techniques
12, 14, 16, 6, 9
A, E
Evaluates classification techniques
12, 14, 16, 6, 9
A, E
Applies unsupervised machine learning techniques
12, 14, 16, 6, 9
A, E
Applies feature selection / analysis techniques
12, 14, 16, 6, 9
A, E
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Elements of machine learning
2
Regression
3
Basics of classification
4
Bayesian classifier
5
Logistic regression
6
Support vector machines
7
Neural networks
8
Convolutional neural networks
9
Decision trees
10
Ensemble methods
11
Feature selection
12
Principal component analysis
13
Clustering
14
Model evaluation
Resources
Bishop, “Pattern Recognition and Machine Learning,” Springer, (1st
edition)
Duda, Hart, and Stork, “Pattern Classification,” Wiley-Interscience, (2nd
edition)
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
X
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
X
7
An ability to communicate effectively
X
8
A recognition of the need for, and an ability to engage in life-long learning
X
9
An understanding of professional and ethical responsibility
X
10
A knowledge of contemporary issues
X
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
