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Information
| Code | CENG0062 |
| Name | Machine Learning for Robotics |
| Term | 2024-2025 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 | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Barış ATA |
| Course Instructor |
1 |
Course Goal / Objective
This course introduces students to the field of machine learning (ML) as applied to robotics.
Course Content
This course covers the use of machine learning techniques in robotics, including supervised and unsupervised learning, reinforcement learning, and deep learning. Topics include robot perception, control, and decision-making using machine learning techniques.
Course Precondition
Basic knowledge of probability theory, linear algebra, and calculus. Familiarity with programming concepts and experience in at least one programming language such as Python, C++, or Matlab.
Resources
"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili.
Notes
"Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understand the principles of machine learning and its applications in robotics. |
| LO02 | Understand the difference between supervised, unsupervised, and reinforcement learning, and their applications in robotics. |
| LO03 | Be able to implement ML algorithms and apply them to robotics problems. |
| LO04 | Be able to evaluate the performance of ML algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 3 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 2 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 2 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 1 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 1 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Machine Learning for Robotics | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Probability and Statistics for Machine Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Supervised Learning: Regression, Decision Trees, Support vector Machines | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Supervised Learning: Neural Networks and Deep Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Unsupervised Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Reinforcement Learning: Introduction to RL and Markov decision process | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Reinforcement Learning: Q-learning and SARSA | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Reading the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Robotics Perception with Machine Learning | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Robotics Perception with Machine Learning II | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Robotics Control with Machine Learning I | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Robotics Control with Machine Learning II | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Robotics Motion Planning with Machine Learning I | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Robotics Motion Planning with Machine Learning II | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 15 | Review | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Reading the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading the lecture notes | 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 | 14 | 14 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||