The objective of this course is to train students so that they perceive Machine Learning algorithms in both logical and mathematical dimensions and develop these algorithms with a programming language within the framework of Artificial Intelligence concept.
Course Content
This course contains; Definition and History of Artificial Intelligence,Machine Learning,Machine Learning,Data conversion, visual conversion, 3-4-5 rule, Introduction to unsupervised learning, Python examples.,DEEP LEARNING
MACHINE VISION,Data Preparation and Data Warehouses,BIG DATA
KNOWLEDGE PRESENTATION
,NATURAL LANGUAGE PROCESSING (NLP)
TEXT MINING (TM)
WEB MINING (WM),AI Agents,Ethical Issues of AI,Unsupervised Learning, Distance, Similarity, Centre of Graphs and AI,Statistical Learning and Model Selection.,Examples of Supervised - Unsupervised Learning Algorithms,Writing our own library for Python: Fuzzy C means algorithm, XİE BENI and other unsupervised learning algorithms quality measures..
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
1. Artificial Intelligence will be able to explain.
16, 3, 9
A
1.1. Defines Artificial Intelligence
1.2. List the components of Artificial Intelligence
2. Will be able to define the concept of unsupervised learning.
9
A
2.1. Explains unsupervised learning
2.2. List the algorithm types of unsupervised learning
3. Will be able to define the concept of supervised learning.
6, 9
A
3.1. Explains supervised learning
3.2. List the algorithm types of supervised learning
4. Will be able to define pattern recognition.
14, 2, 6, 9
A, E
4.1. Explains pattern recognition
4.2 Lists the algorithms of pattern recognition
5. Will be able to define the sub-components of Artificial Intelligence.
16, 9
A, E
5.1. NLP defines
5.2. Defines the concept of robotics
5.3. Defines Text Mining
5.4. Defines Data Mining
5.5. Distinguishes the concepts of Classification and Clustering
Teaching Methods:
14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 3: Problem Baded Learning Model, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Definition and History of Artificial Intelligence
2
Machine Learning
Watch the video and be ready to answer in-class questions.
3
Machine Learning
Watch the video and be ready to answer in class questions.
4
Data conversion, visual conversion, 3-4-5 rule, Introduction to unsupervised learning, Python examples.
Watch the video and be prepared for in-class questions.
5
DEEP LEARNING
MACHINE VISION
Recommended Reading
6
Data Preparation and Data Warehouses
Watch the related video, answer the given questions
7
BIG DATA
KNOWLEDGE PRESENTATION
8
NATURAL LANGUAGE PROCESSING (NLP)
TEXT MINING (TM)
WEB MINING (WM)
9
AI Agents
Watch the related video, answer the given questions
10
Ethical Issues of AI
Watch the video, answer the questions before and in classs
11
Unsupervised Learning, Distance, Similarity, Centre of Graphs and AI
Watch the video, answer the questions before and in classs
12
Statistical Learning and Model Selection.
Video watching and reading
13
Examples of Supervised - Unsupervised Learning Algorithms
14
Writing our own library for Python: Fuzzy C means algorithm, XİE BENI and other unsupervised learning algorithms quality measures.
Prepare Python Environment
Resources
BASIC OF ARTIFICAL INTELLIGENCE
by Philips Coleman, | 2021
AFTER EACH LESSON A READING OR WATCHING TASK WILL BE GIVEN BY THE LECTURER.
Artifical Intelligence: Understanding Future's Language Kindle Edition by Umut Guney (Author) , 2023.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Defines the theoretical issues in the field of information and management.
X
2
Describes the necessary mathematical and statistical methods in the field of information and management.
X
3
Uses at least one computer program in the field of information and management.
X
4
Sustains proficiency in a foreign language requiredor information and management studies.
5
Prepares informatics/software projects and work in a team.
6
Constantly updates himself / herself by following developments in science and technology with an understanding of the importance of lifelong learning through critically evaluating the knowledge and skills that s/he has got.7. Uses theoretical and practical expertise in the field of information and management
7
Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication skills.
X
8
Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication.
9
Adopts organizational / institutional and social ethical values.
10
Within the framework of community involvement adopts social responsibility principles and takes initiative when necessary.
11
Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies.
X
12
Writes software in different platforms such as desktop, mobile, web on its own and / or in a team.
X
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
40
Rate of Final Exam to Success
60
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
15
3
45
Guided Problem Solving
4
1
4
Resolution of Homework Problems and Submission as a Report
7
5
35
Term Project
7
3
21
Presentation of Project / Seminar
1
12
12
Quiz
1
3
3
Midterm Exam
1
6
6
General Exam
1
12
12
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
138
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(138/30)
5
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
ARTIFICAL INTELLIGENCE
-
Spring Semester
3+0
3
5
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. Gökhan SİLAHTAROĞLU
Name of Lecturer(s)
Prof.Dr. Gökhan SİLAHTAROĞLU
Assistant(s)
Aim
The objective of this course is to train students so that they perceive Machine Learning algorithms in both logical and mathematical dimensions and develop these algorithms with a programming language within the framework of Artificial Intelligence concept.
Course Content
This course contains; Definition and History of Artificial Intelligence,Machine Learning,Machine Learning,Data conversion, visual conversion, 3-4-5 rule, Introduction to unsupervised learning, Python examples.,DEEP LEARNING
MACHINE VISION,Data Preparation and Data Warehouses,BIG DATA
KNOWLEDGE PRESENTATION
,NATURAL LANGUAGE PROCESSING (NLP)
TEXT MINING (TM)
WEB MINING (WM),AI Agents,Ethical Issues of AI,Unsupervised Learning, Distance, Similarity, Centre of Graphs and AI,Statistical Learning and Model Selection.,Examples of Supervised - Unsupervised Learning Algorithms,Writing our own library for Python: Fuzzy C means algorithm, XİE BENI and other unsupervised learning algorithms quality measures..
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
1. Artificial Intelligence will be able to explain.
16, 3, 9
A
1.1. Defines Artificial Intelligence
1.2. List the components of Artificial Intelligence
2. Will be able to define the concept of unsupervised learning.
9
A
2.1. Explains unsupervised learning
2.2. List the algorithm types of unsupervised learning
3. Will be able to define the concept of supervised learning.
6, 9
A
3.1. Explains supervised learning
3.2. List the algorithm types of supervised learning
4. Will be able to define pattern recognition.
14, 2, 6, 9
A, E
4.1. Explains pattern recognition
4.2 Lists the algorithms of pattern recognition
5. Will be able to define the sub-components of Artificial Intelligence.
16, 9
A, E
5.1. NLP defines
5.2. Defines the concept of robotics
5.3. Defines Text Mining
5.4. Defines Data Mining
5.5. Distinguishes the concepts of Classification and Clustering
Teaching Methods:
14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 3: Problem Baded Learning Model, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Definition and History of Artificial Intelligence
2
Machine Learning
Watch the video and be ready to answer in-class questions.
3
Machine Learning
Watch the video and be ready to answer in class questions.
4
Data conversion, visual conversion, 3-4-5 rule, Introduction to unsupervised learning, Python examples.
Watch the video and be prepared for in-class questions.
5
DEEP LEARNING
MACHINE VISION
Recommended Reading
6
Data Preparation and Data Warehouses
Watch the related video, answer the given questions
7
BIG DATA
KNOWLEDGE PRESENTATION
8
NATURAL LANGUAGE PROCESSING (NLP)
TEXT MINING (TM)
WEB MINING (WM)
9
AI Agents
Watch the related video, answer the given questions
10
Ethical Issues of AI
Watch the video, answer the questions before and in classs
11
Unsupervised Learning, Distance, Similarity, Centre of Graphs and AI
Watch the video, answer the questions before and in classs
12
Statistical Learning and Model Selection.
Video watching and reading
13
Examples of Supervised - Unsupervised Learning Algorithms
14
Writing our own library for Python: Fuzzy C means algorithm, XİE BENI and other unsupervised learning algorithms quality measures.
Prepare Python Environment
Resources
BASIC OF ARTIFICAL INTELLIGENCE
by Philips Coleman, | 2021
AFTER EACH LESSON A READING OR WATCHING TASK WILL BE GIVEN BY THE LECTURER.
Artifical Intelligence: Understanding Future's Language Kindle Edition by Umut Guney (Author) , 2023.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Defines the theoretical issues in the field of information and management.
X
2
Describes the necessary mathematical and statistical methods in the field of information and management.
X
3
Uses at least one computer program in the field of information and management.
X
4
Sustains proficiency in a foreign language requiredor information and management studies.
5
Prepares informatics/software projects and work in a team.
6
Constantly updates himself / herself by following developments in science and technology with an understanding of the importance of lifelong learning through critically evaluating the knowledge and skills that s/he has got.7. Uses theoretical and practical expertise in the field of information and management
7
Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication skills.
X
8
Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication.
9
Adopts organizational / institutional and social ethical values.
10
Within the framework of community involvement adopts social responsibility principles and takes initiative when necessary.
11
Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies.
X
12
Writes software in different platforms such as desktop, mobile, web on its own and / or in a team.
