Deep learning for media technology, 6 credits

Deep Learning för medieteknik, 6 hp

TNM112

Main field of study

Computer Science and Engineering Media Technology and Engineering

Course level

Second cycle

Course type

Programme course

Examiner

Gabriel Eilertsen

Director of studies or equivalent

Camilla Forsell

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 160 h
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CMEN Media Technology and Engineering, Master of Science in Engineering 9 (Autumn 2023) 2 2 Swedish/English Norrköping E
6CMEN Media Technology and Engineering, Master of Science in Engineering (Advanced Techniques for Audio and Image Media) 9 (Autumn 2023) 2 2 Swedish/English Norrköping E
6CMEN Media Technology and Engineering, Master of Science in Engineering (Visualization) 9 (Autumn 2023) 2 2 Swedish/English Norrköping E

Main field of study

Computer Science and Engineering, Media Technology and Engineering

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master of Science in Media Technology and Engineering

Entry requirements

Probability theory and statistics, calculus, linear algebra, and basic programming. Foundations in machine learning is beneficial but not required.

Prerequisites

Probability theory and statistics, calculus, linear algebra, and basic programming. Foundations in machine learning is beneficial but not required.

Intended learning outcomes

The course teaches fundamental techniques in deep learning, theoretical as well as practical, and gives an overview of modern techniques and applications related to deep learning for images, graphics and sound.

After finishing the course, the student should be able to:

  • describe fundamental techniques for how to construct and optimize artificial neural networks, and show understanding of how theoretical concepts relate to practical situations,
  • demonstrate knowledge on modern methods for how neural networks can be designed, optimized and used in different contexts within computer vision, image processing, computer graphics, natural language processing and visualization,
  • use existing deep learning tools for solving classification and and regression problems for digital media,
  • use techniques for testing and improving the performance of neural networks, including concepts such as detection of overfitting and techniques for increasing generalization capability,
  • formulate and solve simpler problems from start, including data collection, selection of techniques, and analysis of the results,
  • reason around the consequences of an increasing use of deep learning in the society, both in terms of opportunities as well as problematic questions.

Course content

Overview of machine learning. Artificial neural networks (ANN) and the term deep learning. Learning paradigms (supervised/unsupervised/semi-supervised/self-supervised/reinforcement learning). Optimization of ANNs (back-propagation, stochastic gradient descent, momentum, batch normalization). Regularization (augmentation, drop-out, early-stopping). Data (images/video/sound/3D, representation, training/testing, bias, adversarial examples). Architectures (convolutional networks, auto-encoders, recurrent networks, recursive networks). Generative deep learning. Applications (computer vision, image processing, computer graphics, natural language processing, visualization) and implications (societal impacts, ethics, bias).

Teaching and working methods

The course consists of lectures, lessons and laboratory work. The lectures teaches theory and explains around the intensive research that has been conducted within deep learning over the last decade. The lessons treat the practical aspects of using tools for solving problems with deep learning; knowledge which is then applied in the laboratory work for constructing and optimizing neural networks.

Examination

TEN1Written Examination3 creditsU, 3, 4, 5
LAB1Laboratory Work3 creditsU, G

Grades

Four-grade scale, LiU, U, 3, 4, 5

Other information

About teaching and examination language

The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows: 

  • If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly in English. Examination language is Swedish, but parts of the examination can be in English.
  • If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
  • If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.

Other

The course is conducted in a manner where both men's and women's experience and knowledge are made visible and developed. 

The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point. 

The course is campus-based at the location specified for the course, unless otherwise stated under “Teaching and working methods”. Please note, in a campus-based course occasional remote sessions could be included.  

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department

Institutionen för teknik och naturvetenskap

Course literature

Additional literature

Books

Code Name Scope Grading scale
TEN1 Written Examination 3 credits U, 3, 4, 5
LAB1 Laboratory Work 3 credits U, G

Additional literature

Books

Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron, (2016) Deep learning. Cambridge, MA : MIT Press, [2016]

ISBN: 9780262035613, 0262035618

https://www.deeplearningbook.org/

Michael Nielsen, (2019) Neural Networks and Deep Learning Online

http://neuralnetworksanddeeplearning.com/

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)

                            
1.2 Fundamental engineering knowledge (G1X level)

                            
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)

                            
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)

                            
1.5 Insight into current research and development work

                            
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving

                            
2.2 Experimentation, investigation, and knowledge discovery

                            
2.3 System thinking

                            
2.4 Attitudes, thought, and learning

                            
2.5 Ethics, equity, and other responsibilities

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork

                            
3.2 Communications

                            
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context

                            
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management

                            
4.4 Designing

                            
4.5 Implementing

                            
4.6 Operating

                            
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects

                            

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