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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT170F valid from Autumn 2016

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General
Aim
  • The study circle introduces doctoral students to recent advances in deep learning. Focus is on using deep learning in practice.
Contents
  • The content is based on the doctoral students' choice and active participations. Possible subtopics include Autoencoders, Convolutionan Networks, Structured Probabilistic Models, Restricted Boltzmann Machines, Recurrent and Recursive Nets, Deep Reinforcement Learning, Tensorflow, Deep Learning using GPUs, Stacked Denoising Autoencoders, DL for Natural Language Processing, Deconvolutionan networks, Optimization of Deep Networks.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • understand how deep networks can be constructed and trained.
    know typical components of a deep network such as RBMs, autoencoders, RNNs.
    understand how typical network and training parameters influence the performance of the networks and their training speed.

Competences and Skills
  • For a passing grade the doctoral student must
  • be able to construct and train a deep neural network using an existing software platform, such as Tensorflow, Theano or similar.
    be able to understand how to map a practical problem to a deep network architecture.
    read and understand research articles in the field of deep learning.
    be able to choose efficient network training methods.
Judgement and Approach
  • For a passing grade the doctoral student must
Types of Instruction
  • Lectures
  • Laboratory exercises
Examination Formats
  • Written assignments
  • Seminars given by participants
  • For a passing grade the doctoral student should give a lecture on a deep learning topic and construct a suitable hand-in assignment for the other course participants. The student should also solve at least half of the exercises presented in the course and upload solutions and code to a common code repository.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • The participants are assumed to have taken a course in basic machine learning or have corresponding knowledge.
Selection Criteria
Literature
  • Goodfellow, B. & Courville: Deep Learning (manuscript).
  • Book manuscript: https://github.com/HFTrader/DeepLearningBook/blob/master/DeepLearningBook.pdf
Further Information
Course code
  • FRT170F
Administrative Information
  • 2017-06-01
  • Professor Thomas Johansson

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