EDAN95F

Active

Applied Machine Learning

7.5

Course given jointly for second and third cycle

EDAN95, EDAN96

7121 (Computer Science (LTH))

EDAN95, EDAN96

 -10-15

Professor Thomas Johansson

English

Every autumn semester

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.

* 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

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

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

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

Lectures

Laboratory exercises

Written exam

The written examination can be replaced by an oral exam given mutual agreement

Failed, pass

EDAA01 Programming - Second Course or similar

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.

EDAN95F

 -10-15

Professor Thomas Johansson

Autumn 2018

2024-11-04

2025-01-31

Lund

Full time

Maj Stenmark <maj.stenmark@cs.lth.se>
Elin A. Topp <elin_a.topp@cs.lth.se>

Associate senior lecturer Maj Stenmark

Contact the 1st course responsible teacher (Maj Stenmark, via e-mail) AT THE LATEST on September 30 to make sure to take part in the selection process according to the following priorities: 1. PhD students with the CS department, 2. other LTH departments, 3. other LU departments, 4. external, 5. other participants (non-PhD student). The number of places on PhD education level is determined based on the number of students from undergraduate (MSc) level. The supervisor needs to agree for the PhD student to join the course.

Procedures for handin-assignments and exercises for PhD students can be differing from those for undergraduate students and are subject to agreement with the course responsible teacher

2024-05-30

Maj Stenmark