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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for Course EDAN95F Applied Machine Learning

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General
  • EDAN95F
  • Active
Course Name
  • Applied Machine Learning
Course Extent
  • 7.5
Type of Instruction
  • Course given jointly for second and third cycle
Administrative Information
  • 7121 (Computer Science (LTH))
  •  -10-15
  • Professor Thomas Johansson

Current Established Course Syllabus

General
  • English
  • Every autumn semester
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.
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
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
Types of Instruction
  • Lectures
  • Laboratory exercises
Examination Formats
  • Written exam
  • The written examination can be replaced by an oral exam given mutual agreement
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • EDAA01 Programming - Second Course or similar
Selection Criteria
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.
Further Information
Course code
  • EDAN95F
Administrative Information
  •  -10-15
  • Professor Thomas Johansson

All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Autumn 2018 2018‑10‑12 16:27:33 2018‑10‑15 08:28:40 2018‑10‑15

Current or Upcoming Published Course Occasion

No matching course occasion was found.

All Published Course Occasions

5 course occasions.

Course code ▽ Course Name ▽ Division ▽ Established ▽ Course syllabus valid from ▽ Start Date ▽ End Date ▽ Published ▽
EDAN95F Applied Machine Learning Computer Science (LTH) 2019‑09‑26 Autumn 2018 2019‑11‑04 2019‑12‑31 2019‑09‑26
EDAN95F Applied Machine Learning Computer Science (LTH) 2020‑09‑29 Autumn 2018 2020‑11‑02 2020‑12‑31 2020‑09‑29
EDAN95F Applied Machine Learning Computer Science (LTH) 2021‑08‑16 Autumn 2018 2021‑11‑01 2021‑12‑31 2021‑08‑16
EDAN95F Applied Machine Learning Computer Science (LTH) 2022‑06‑08 Autumn 2018 2022‑10‑31 2023‑01‑31 2022‑06‑08
EDAN95F Applied Machine Learning Computer Science (LTH) 2023‑06‑15 Autumn 2018 2023‑10‑30 2024‑01‑31 2023‑06‑15

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