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

General

Aim

To demonstrate understanding of the concepts and show the ability to use the methods of Chapters 14-17 in the course book.

Contents

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

Examination Formats

Admission Requirements

Assumed Prior Knowledge

Selection Criteria

Literature

Literature:
Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Unsupervised learning techniques. 2019. ISBN 9781492032649.

Further Information

Course code

Administrative Information

All Published Course Occasions for the Course Syllabus

1 course occasion.

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