Course Syllabus for
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.
Types of instruction: Lectures, laboratory exercises
Examination format: Written exam.
The written examination can be replaced by an oral exam given mutual agreement
Grading scale: Failed, pass
Examiner: Head of department Elin A. Topp
Admission Details
Assumed prior knowledge: EDAA01 Programming - Second Course or similar
Course Occasion Information
Start date: 2019-11-04
End date: 2019-12-31
Course pace: Full time
Application Information
contact the course responsible teacher (e-mail)
Course coordinators:
Web page: cs.lth.se/edan95
Other information: 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