TÜRKÇE
ENGLISH
Information
| Code | MN0024 |
| Name | Artificial Intelligence in Material Science |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
To be able to apply artificial intelligence models in materials science.
Course Content
Python Basics, Programming in Python, Introduction to Artificial Intelligence, Problem Solving, Knowledge and Reasoning, Acting Logically, Uncertain Knowledge and Reasoning, Learning, Reinforcement Learning, Q-Learning, Deep Q-Learning, Envrionement Design, Deep Convolutional Q-Learning, AI Applications in Material Science.
Course Precondition
To have knowledge about computer programming.
Resources
1-) Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 3th edition, Pearson, (2016) 2-) Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, 1st edition, O’Reilly Media Inc., (2016)
Notes
1-) Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 3th edition, Pearson, (2016) 2-) Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, 1st edition, O’Reilly Media Inc., (2016)
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learning the Python programmin language. |
| LO02 | Learning artificial intelligence techniques. |
| LO03 | Understands the difference between data and knowledge. |
| LO04 | Can apply various learning methods. |
| LO05 | Can apply artificial intelligence and machine learning techniques to material science. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Based on the qualifications gained during PhD studies, develops and deepens the current and advanced knowledge in the area by unique means of thinking and / or research at mastery level and comes up with original definitions which bring about novelty to the area. | 3 |
| PLO02 | Beceriler - Bilişsel, Uygulamalı | Can effectively use the equipment used in the field. | |
| PLO03 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Selects experimental measurement methods of various physical quantities and uses instruments in accordance with their sensitivity limits. | |
| PLO04 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets experimental and observational results. | |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Can draw conclusions from the information obtained during the preparation for the PhD qualifying exam. | |
| PLO06 | Bilgi - Kuramsal, Olgusal | Can interpret the information acquired about the field orally and in writing. | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Uses mathematical methods related to the field of study. | 3 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have knowledge about the logic, design, conclusion and dissemination of results of scientific research. | 3 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Uses the theoretical and applied knowledge gained in the field of materials and nanotechnology at the level of expertise. | 3 |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Gains high-level skills in using research methods in studies related to materials science and nanotechnology. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Develops a scientific method that brings innovation to science. | 3 |
| PLO12 | Yetkinlikler - Alana Özgü Yetkinlik | Makes critical analysis, synthesis and evaluation of new ideas related to the field. | 3 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Can carry out independent research on a specific topic related to materials and nanotechnology. | 4 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Be able to lead in the execution of interdisciplinary studies. | 5 |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Follows the developments in the her/his field of study and constantly renews herself/himself. | 4 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Calculate the predictions of the theories and compare them with the experimental results. | 3 |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Comprehends the interdisciplinary interaction that the field of study is related to. | 5 |
| PLO18 | Yetkinlikler - Alana Özgü Yetkinlik | He/she shares his/her own ideas and suggestions regarding the problems in the field of study with groups in and outside the field by supporting them with quantitative and qualitative data. | 4 |
| PLO19 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Can develop original solutions for problems in the field. | 4 |
| PLO20 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Can prepare a scientific article and publish scientific articles in international refereed journals. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Python Basics | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Programming in Python | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 3 | Introduction to Artificial Intelligence | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 4 | Problem Solving | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 5 | Knowledge and Reasoning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 6 | Acting Logically | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 7 | Uncertain Knowledge and Reasoning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 8 | Midterm exams | Ölçme Yöntemleri: Sözlü Sınav |
|
| 9 | Learning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 10 | Reinforcement Learning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 11 | Q-Learning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Deep Q-Learning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 13 | Envrionement Design | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 14 | Deep Convolutional Q-Learning | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 15 | AI Applications in Material Science | Study the relevant chapter of the textbook. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 16 | Final Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Final Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||