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Blekinge Institute of Technology
Department of Computer Science

Revision: 2
Reg.no: BTH-4.1.14-0598-2025


Course syllabus

Deep Machine Learning

Deep Machine Learning

6 credits (6 högskolepoäng)

Course code: DV2646
Main field of study: Computer Science
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2025-08-22
Approved: 2025-08-22

1. Descision

This course is established by Dean 2023-05-23. The course syllabus is approved by Head of Department of Computer Science 2025-08-22 and applies from 2025-08-22.

2. Entry requirements

Admission to the course requires completed course Machine Learning, 6 credits. English 6.

3. Objective and content

3.1 Objective

This course introduces fundamental and advanced concepts in deep machine learning, with a focus on mathematical foundations, modeling, and the implementation of neural networks. The course also covers how deep machine learning is applied in practice, including training, validation, testing, and optimization of models.

3.2 Content

  • Artificial neural networks,
  • Deep Learning Training Components,
  • Convolutional Neural Networks,
  • Deep machine learning,
  • Deep recurrent neural networks,
  • Advanced classification methods based on deep machine learning,
  • Explainability and interpretability in deep machine learning,
  • Long Short-Term Memory (LSTM) networks,
  • Autoencoders: encoding and decoding,
  • Adversarial Learning and Generative Adversarial Networks (GANs),
  • Applications of deep learning methods in different fields.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding 

On completion of the course, the student will be able to:

  • explain fundamentals of deep machine learning
  • possess advanced knowledge within the field of deep machine learning

4.2. Competence and skills 

On completion of the course, the student will be able to:

  • design, develop and apply deep machine learning methods when carrying out research and development activities for the relevant problems
  • implement deep machine learning solutions for relevant problems
  • Train, validate, test, and fine-tune hyperparameters for deep machine learning methods
  • Evaluate and compare the performance of deep machine learning methods

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • critically review relevant literature on deep machine learning methods

5. Learning activities

By combining theory with practical exercises, the course aims to provide students with the skills required to understand, implement, and evaluate models in deep learning.

This is complemented by exercises and laboratory work, giving students the opportunity to develop general skills, abilities, and approaches in line with the course’s learning objectives.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2605 Written Assignment 1 1 credits GU
2615 Written Assignment 2 1 credits GU
2626 Project 4 credits AF

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow, 1st edition
Publisher: Pearson
Author: Magnus Ekman
Year: 2022

Deep Learning
Publisher: MIT Press
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Year: 2016

Deep Learning with Python Second Edition
Author: Francois Chollet
Publisher: Manning
Year: 2021