CENG027 Machine Learning for Robotics

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Code CENG027
Name Machine Learning for Robotics
Semester . Semester
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 Goal

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