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Advanced Topics in Machine Learning


Technical University of Denmark


General course objectives:
To introduce the student to new trends in statistical signal processing and machine learning.

Learning objectives:
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

Contents:
The course introduces new trends and advanced topics in machine learning. The course covers key topics in machine learning such as Bayesian parametric and non-parametric inference, optimization, latent variable models, kernel methods, and deep learning. 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. Typical applications include: Bio-medical, audio, multimedia, and topic modeling as well as collaborative filtering and monitoring systems.

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Course organizer
Morten , Jesper Løve
Place/Venue
Anker Engelunds Vej 1
City
2800 Kgs. Lyngby
Country
Denmark
Workload
2.5
Link
http://kurser.dtu.dk/course/02901