Self-routing autonomous robots by IUE engineers
An autonomous robot that can re-route using artificial intelligence, when it encounters an obstacle, has been developed with the project ...
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Department of Mechatronics Engineering
CE 344 | Course Introduction and Application Information
| Course Name |
Advanced Machine Learning
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
CE 344
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
First Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | DiscussionProblem SolvingQ&ACritical feedbackLecture / Presentation | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | The objective of this course is to provide advanced knowledge on the state of the art in machine learning. Both fundamental and advanced properties of machine learning algorithms as well as practical applications will be discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course Description | Course discusses several advanced techniques such as training data collection, learning in order to extract statistical structure from data, over-fitting, parametric models and parameter selection, validation, regression, classification, nonparametric models, clustering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation | Learning Outcome |
| 1 | Introduction to machine learning. Probability review | Chapter 1-2. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 2 | Generative models for discrete data. Gaussian models | Chapter 3-4. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 3 | Bayesian and frequentist statistics | Chapter 5-6. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 4 | Linear and logistic regression | Chapter 7-8. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 5 | Generalized linear models and the exponential family | Chapter 9. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 6 | Graphical models: Markov random fields and Bayes nets | Chapter 10 and 19. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 7 | Mixture models and the EM algorithm | Chapter 11. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 8 | Latent linear and sparse linear models | Chapter 12-13. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 9 | Markov and hidden Markov models | Chapter 17. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 10 | Midterm exam | ||
| 11 | Exact inference for graphical models. Variational inference. | Chapter 20-21-22. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 12 | Monte Carlo and Markov Chain Monte Carlo inference | Chapter 23-24. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 13 | Kernel models | Chapter 14. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 14 | Clustering | Chapter 25. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 | |
| 15 | Semester Review | ||
| 16 | Final Exam |
| Course Notes/Textbooks | Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012, ISBN: 9780262018029 |
| Suggested Readings/Materials | Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 9780387310732. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing | LO 1 | LO 2 | LO 3 | LO 4 | LO 5 |
| Participation | |||||||
| Laboratory / Application | |||||||
| Field Work | |||||||
| Quizzes / Studio Critiques |
4
|
30
|
|||||
| Portfolio | |||||||
| Homework / Assignments | |||||||
| Presentation / Jury | |||||||
| Project | |||||||
| Seminar / Workshop | |||||||
| Oral Exams | |||||||
| Midterm |
1
|
30
|
|||||
| Final Exam |
1
|
40
|
|||||
| Total |
| Weighting of Semester Activities on the Final Grade |
5
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
3
|
42
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
4
|
2
|
8
|
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
25
|
25
|
| Final Exam |
1
|
25
|
25
|
| Total |
148
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems |
-
|
-
|
-
|
-
|
-
|
|
| 2 |
To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. |
-
|
-
|
-
|
-
|
-
|
|
| 3 |
To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose. |
-
|
-
|
-
|
-
|
-
|
|
| 4 |
To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively. |
-
|
-
|
-
|
-
|
-
|
|
| 5 |
To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. |
-
|
-
|
-
|
-
|
-
|
|
| 6 |
To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. |
-
|
-
|
-
|
-
|
-
|
|
| 7 |
To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. |
-
|
-
|
-
|
-
|
-
|
|
| 8 |
To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. |
-
|
-
|
-
|
-
|
-
|
|
| 9 |
To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. |
-
|
-
|
-
|
-
|
-
|
|
| 10 |
To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. |
-
|
-
|
-
|
-
|
-
|
|
| 11 |
Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) |
-
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-
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-
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-
|
-
|
|
| 12 |
To be able to use the second foreign language at intermediate level. |
-
|
-
|
-
|
-
|
-
|
|
| 13 |
To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering. |
-
|
-
|
-
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-
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-
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|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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