Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT250F valid from Autumn 2020

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  • To demonstrate understanding of the concepts and show the ability to use the methods of Chapters 14-17 in the course book.
  • Deep computer vision using convolutional neural networks, processing sequences using RNNs and CNNs, natural language processing with RNNs and attention, representation, generation using autoencoders and GANs.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • demonstrate understanding for the basic concepts and methods presented in Chapters 14-17 in the course book. This includes programming in Python, CNNs, RNNs, Autoencoders and GANs.
Competences and Skills
  • For a passing grade the doctoral student must
  • participate actively in the discussions on the different chapters
    host a chapter-session and complete a project, which includes hands-on implementation of ML-algorithms in Python.
Judgement and Approach
  • For a passing grade the doctoral student must
  • demonstrate understanding for the suitability of different ML-algorithms to bench-mark problems and be able to choose parts of the chapter content to present at the chapter-session they are hosting as well as organize the meeting.
Types of Instruction
  • Seminars
  • Project
  • Self-study literature review
Examination Formats
  • Seminars given by participants
  • Sufficient participation in the seminars and the discussions.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
  • None
  • Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Unsupervised learning techniques. 2019. ISBN 9781492032649.
Further Information
  • The course is given upon request, if sufficient demand is present.
Course code
  • FRT250F
Administrative Information
  • 2021-03-01
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2020‑11‑01 (approximate) 2021‑02‑09

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