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02459 Machine Learning for Signal Processing |
| | |  | Danish title:
| Machine learning og signalbehandling | Language:
| | Point
(ECTS )
| 5 | Course type:
| Advanced course
| | Taught under open university |
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| Schedule:
| F1B
The course starts with introductory lectures and exercises in F1B (usually 3-4 weeks). Next, project work is carried out in groups of 2-3 students, and project meetings are arranged with the supervisor.
| Scope and form: | Lectures and independent projects in groups of 2 students | Duration of Course:
| 13 weeks | Date of examination:
| Special day
Oral poster presentation at the end of the semester. Written report to be handed in by the end of the semester. | Type of assessment:
| | Aid:
| | Evaluation: | | Not applicable together with: | | Qualified Prerequisites: | | Optional Prerequisites: | , |
| Participants restrictions: | Maximum: 30 | | | General course objectives:
| To provide knowledge of current research topics in machine learning and secondarily signal processing. To provide hands on experience in an application from the technical or scientific domain.
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| Learning objectives: | | A student who has met the objectives of the course will be able to: | - Define own learning objectives for the project
- Collect scientific knowledge and data related to the project topic based on a specific project proposal
- Carry out a well-founded delimitation of the project and formulate specific hypotheses and aims
- Organize and coordinate the work in the project group
- Plan and carry out the course of the project in collaboration wit the project supervisor
- Design a machine learning based system starting from analysis of the problem and the project aims, and further select relevant algorithms and methods
- Assess and summarize the project results in relation to aims, methods and available data
- Carry out the project and interpret results by use of Matlab
- Structure and write a final short technical report including problem formulation, description of methods, experiments, evaluation and conclusion
- Presentation of methods and results at meetings with project supervisor and fellow students
- Organize and present project results at the final poster presentation
| Content:
| The course starts with lectures on current research subjects, e.g., prediction of time signals, neural networks, hidden Markov models and Kalman filters for sequential data, Bayesian modeling and classification, independent component analysis, segregation, and analysis of audio signals. The participants carry out a project within the presented topics. |
| Remarks:
| This course is an advanced course in machine learning and signal processing and part of the focus area Machine Learning and Signal Processing of the Master of Mathematical Modelling and Computing program.
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| Green challenge participation:
| Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More information |
| Responsible:
| , 321, 015, (+45) 4525 3923,
, 321, 012, (+45) 4525 3889,
, 321, 115, (+45) 4525 3895,
| Department:
| 02 Department of Informatics and Mathematical Modeling | Home page:
| | Registration Sign up:
| At CampusNet | Keywords: | signal processing, machine learning, Bayesian analysis, application of machine learning for signal processing |
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| | Last updated:
June 18, 2012 |
See course in DTU Course base
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