Deep Learning - Study Circle
Studiecirkel i djupa neuralnät

FRT170F, 7.5 credits

Valid from: Autumn 2016
Decided by: Professor Thomas Johansson
Date of establishment: 2017-06-01

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English

Aim

The study circle introduces doctoral students to recent advances in deep learning. Focus is on using deep learning in practice.

Goals

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.

Course 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.

Course Literature

Goodfellow, B. & Courville: Deep Learning (manuscript).
Book manuscript: https://github.com/HFTrader/DeepLearningBook/blob/master/DeepLearningBook.pdf

Instruction Details

Types of instruction: Lectures, laboratory exercises

Examination Details

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.
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: The participants are assumed to have taken a course in basic machine learning or have corresponding knowledge.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: http://www.control.lth.se/Education/DoctorateProgram/deep-learning-study-circle.html