Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|---|---|---|---|---|
ARTIFICAL INTELLIGENCE | MIS3212182 | Spring Semester | 3+0 | 3 | 5 |
Course Program | Cuma 08:00-08:45 Cuma 09:00-09:45 Cuma 10:00-10:45 |
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. | 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 | MIS3212182 | Spring Semester | 3+0 | 3 | 5 |
Course Program | Cuma 08:00-08:45 Cuma 09:00-09:45 Cuma 10:00-10:45 |
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. | X |
Assessment Methods
Contribution Level | Absolute Evaluation | |
Rate of Midterm Exam to Success | 40 | |
Rate of Final Exam to Success | 60 | |
Total | 100 |