02901 Advanced Topics in Machine Learning
|Avancerede emner indenfor machine learning|
|Ph.D.- Mathematics, Physics and Informatics|
|Taught under open university|
The course runs in August. See the course homepage for specific dates.
Scope and form:
Lectures, exercises (Matlab), mini-project.
Duration of Course:
|[The Course is not following DTUs normal Schedule]|
Type of assessment:
General course objectives:
To introduce the student to new trends in statistical signal processing and machine learning.
|A student who has met the objectives of the course will be able to:|
- Comprehend and apply advanced methods within machine learning
- Collect scientific knowledge and data related to topics covered in the course
- Formulate and carry out a mini-project related to one or more of the covered course topics (preferably within the scope of the student’s PhD project)
- Design a complex machine learning system based on an analysis of the problem and the project aims
- Implement the machine learning system
- Evaluate the performance of the machine learning system
- Assess and summarize the mini-project results in relation to aims, methods and available data
- Disseminate the project results in a technical report
The course introduces new trends and advanced topics in machine learning. The course covers key topics in machine learning including Bayesian parametric and non-parametric inference, optimization, low rank approximations and kernel methods. The course consists of lectures and exercises, and is followed up by a mini-project presented in a written report. We encourage that students apply the methods taught to data relevant for their PhD project. Current possible topics are: Bayesian methods, latent variable modeling, sparse representations and kernel methods. Typical applications include: Bio-medical, audio, multimedia, and topic modeling as well as collaborative filtering and monitoring systems.
|, 321, 015, (+45) 4525 3923,
, 321, 118, (+45) 4525 3900,
|02 Department of Informatics and Mathematical Modeling|
Registration Sign up:
|At the department|
Sign up with secretary Marian Solrun Adler, firstname.lastname@example.org, 45253920 Deadline: one week prior to the beginning of the course
|machine learning, Bayesiansk inferens, statistical signal processing, non-linear methods|
April 21, 2012|
See course in DTU Course base