Applied Machine Learning
Tillämpad Maskininlärning

EDAN95F, 7.5 credits

Valid from: Autumn 2018
Decided by: Professor Thomas Johansson
Date of establishment: 2018-10-15

General Information

Division: Computer Science (LTH)
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course codes: EDAN95, EDAN96
Teaching language: English

Aim

To give an introduction to several subdomains of machine learning and to give an orientation about fundamental methods and algorithms within these domains. To convey knowledge about breadth and depth of the domain.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• in an examination (oral or written) display basic knowledge concerning theories and methods related to the following subdomains:
• unsupervised and supervised learning, classification and regression
• neural networks, including convolutional neural networks, recurrent neural networks and deep learning
• bayesian learning
• reinforcement learning
• support vector machines, decision trees, random forests, ensemble methods

Competences and Skills

For a passing grade the doctoral student must

• complete a number of assignments based on problems related to some of the previously mentioned subdomains and demonstrating the ability to:
• evaluate and prepare the data
• select and train a model
• evaluate the outcome and fine-tune the model

Judgement and Approach

For a passing grade the doctoral student must

• * be able to judge suitability of a given machine learning method to a given problem,
• * understand limitations of applicability of machine learning methods

Course Contents

* unsupervised and supervised learning, classification and regression * neural networks, including convolutional neural networks, recurrent neural networks and deep learning * bayesian learning * reinforcement learning * support vector machines, decision trees, random forests, ensemble methods * hardware and software architectures for machine learning, parallelisation, use of GPUs

Course Literature

• Murphy, Kevin P.: Machine Learning, A Probabilistic Perspective.. ISBN 9780262018029.
• Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. MIT Press, 2016. ISBN 9780262035613.
• Hands-On Machine Learning with Scikit-Learn and TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems. ISBN 9781491962299.

Instruction Details

Types of instruction: Lectures, laboratory exercises

Examination Details

Examination format: Written exam. The written examination can be replaced by an oral exam given mutual agreement
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: EDAA01 Programming - Second Course or similar

Course Occasion Information

Contact and Other Information

Course coordinator: Elin A. Topp <elin_a.topp@cs.lth.se>
Web page: cs.lth.se/edan95