Veidlapa Nr. M-3 (8)
Study Course Description
Practical Applications of Artificial Intelligence
Course Code
SZF_212
Branch of Science
Other social sciences
ECTS
3.00
Target Audience
Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Linda Alksne
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Dzirciema street 16, Rīga, szf@rsu.lv
About Study Course
Objective
Familiarise students with the basic principles and applications of artificial intelligence (AI) and high performance computing (HPC), with a particular focus on computer vision and natural language processing (NLP).
Preliminary Knowledge
Students should have experience in Python programming, using libraries as NumPy, Pandas and TensorFlow/Pytorch. Be able to work with machine learning models, especially neural networks, and have an understanding of the basics of computer vision (CNN, image processing) and natural language processing (tokenization, Word embeddings, Transformer models).
Learning Outcomes
Knowledge
1.Will be familiar with theoretical basics in computer vision and/or natural language processing (NLP)
2.Will be able to apply neural network and machine learning techniques
3.Will know image and word processing algorithms
4.Will be familiar with the operating principles of deep learning models such as CNN and Transformers Architecture
5.Will be able to apply artificial intelligence and machine learning tools (TensorFlow, Pytorch, OpenCV, Spacy)
Skills
1.Practical application of image and word processing techniques
2.Data analysis and pre-processing for model training
3.Training and optimisation of neural network models
4.Python programming and application of libraries to computer vision and NLP
5.Conducting experiments and interpretation of results
Competences
1.Ability to develop and adapt computer vision and NLP solutions to specific needs
2.Critical thinking and problem-solving skills
3.Ability to analyze and interpret large image and text data
4.Scientific research and technical reporting skills
5.Cooperation in cross-disciplinary projects and real-world application of AI technologies
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Self-assessment test on computer vision |
20.00% from total grade
|
10 points
|
|
Self-assessment test on the subject |
||
|
2.
Self-assessment test on topic NLP |
20.00% from total grade
|
10 points
|
|
3.
Self-assessment test on topic HPC |
20.00% from total grade
|
10 points
|
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Semester paper |
40.00% from total grade
|
Test
|
Study Course Theme Plan
FULL-TIME
Part 3
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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2
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Topics
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Computer vision
Description
Computer vision is an artificial intelligence sub-industry dedicated to processing and analyzing visual information, allowing computers to “see” and interpret images or videos. This field combines a variety of techniques from image processing, deep learning and statistics to develop solutions to problems such as object recognition, image classification, facial recognition, autonomous transport control and medical imaging analysis. |
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Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Study room
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2
|
Topics
|
Computer vision
Description
Computer vision is an artificial intelligence sub-industry dedicated to processing and analyzing visual information, allowing computers to “see” and interpret images or videos. This field combines a variety of techniques from image processing, deep learning and statistics to develop solutions to problems such as object recognition, image classification, facial recognition, autonomous transport control and medical imaging analysis. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Computer vision
Description
Computer vision is an artificial intelligence sub-industry dedicated to processing and analyzing visual information, allowing computers to “see” and interpret images or videos. This field combines a variety of techniques from image processing, deep learning and statistics to develop solutions to problems such as object recognition, image classification, facial recognition, autonomous transport control and medical imaging analysis. |
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Computer vision
Description
Computer vision is an artificial intelligence sub-industry dedicated to processing and analyzing visual information, allowing computers to “see” and interpret images or videos. This field combines a variety of techniques from image processing, deep learning and statistics to develop solutions to problems such as object recognition, image classification, facial recognition, autonomous transport control and medical imaging analysis. |
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Test
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
2
|
Topics
|
Computer vision
Description
Computer vision is an artificial intelligence sub-industry dedicated to processing and analyzing visual information, allowing computers to “see” and interpret images or videos. This field combines a variety of techniques from image processing, deep learning and statistics to develop solutions to problems such as object recognition, image classification, facial recognition, autonomous transport control and medical imaging analysis. |
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Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Study room
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2
|
Topics
|
Natural language processing
Description
Natural language processing (NLP) is a subbranch of artificial intelligence that deals with computers’ ability to understand, analyze and generate human language. This area combines linguistics, machine learning and deep learning to develop technologies that allow computers to process text and speech in a similar way to humans. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Natural language processing
Description
Natural language processing (NLP) is a subbranch of artificial intelligence that deals with computers’ ability to understand, analyze and generate human language. This area combines linguistics, machine learning and deep learning to develop technologies that allow computers to process text and speech in a similar way to humans. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
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2
|
Topics
|
Natural language processing
Description
Natural language processing (NLP) is a subbranch of artificial intelligence that deals with computers’ ability to understand, analyze and generate human language. This area combines linguistics, machine learning and deep learning to develop technologies that allow computers to process text and speech in a similar way to humans. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Natural language processing
Description
Natural language processing (NLP) is a subbranch of artificial intelligence that deals with computers’ ability to understand, analyze and generate human language. This area combines linguistics, machine learning and deep learning to develop technologies that allow computers to process text and speech in a similar way to humans. |
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Unaided Work
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Modality
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Location
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Contact hours
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|---|---|---|
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Off site
|
E-Studies platform
|
1
|
Topics
|
Natural language processing
Description
Natural language processing (NLP) is a subbranch of artificial intelligence that deals with computers’ ability to understand, analyze and generate human language. This area combines linguistics, machine learning and deep learning to develop technologies that allow computers to process text and speech in a similar way to humans. |
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
HPC
Description
HPC (from English High performance computing) allows you to solve complex computing tasks in less time. Computing occurs on computers (shared memory mainframe, computing cluster) made up of many parallel processors. Today, HPC is an integral part of any modern university and is used for a variety of needs – from engineering tasks in the MathWorks MATLAB environment to big data (Big data) analytics and machine learning. Most HPC systems are built as computing clusters, consisting of many separate servers connected to a fast computer network (such as Infiniband). The HPC cluster is suitable for both parallel computing, ensuring the parallel execution of one large task, and distributive computing, performing many independent tasks on individual servers or processors. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
HPC
Description
HPC (from English High performance computing) allows you to solve complex computing tasks in less time. Computing occurs on computers (shared memory mainframe, computing cluster) made up of many parallel processors. Today, HPC is an integral part of any modern university and is used for a variety of needs – from engineering tasks in the MathWorks MATLAB environment to big data (Big data) analytics and machine learning. Most HPC systems are built as computing clusters, consisting of many separate servers connected to a fast computer network (such as Infiniband). The HPC cluster is suitable for both parallel computing, ensuring the parallel execution of one large task, and distributive computing, performing many independent tasks on individual servers or processors. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
HPC
Description
HPC (from English High performance computing) allows you to solve complex computing tasks in less time. Computing occurs on computers (shared memory mainframe, computing cluster) made up of many parallel processors. Today, HPC is an integral part of any modern university and is used for a variety of needs – from engineering tasks in the MathWorks MATLAB environment to big data (Big data) analytics and machine learning. Most HPC systems are built as computing clusters, consisting of many separate servers connected to a fast computer network (such as Infiniband). The HPC cluster is suitable for both parallel computing, ensuring the parallel execution of one large task, and distributive computing, performing many independent tasks on individual servers or processors. |
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Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
HPC
Description
HPC (from English High performance computing) allows you to solve complex computing tasks in less time. Computing occurs on computers (shared memory mainframe, computing cluster) made up of many parallel processors. Today, HPC is an integral part of any modern university and is used for a variety of needs – from engineering tasks in the MathWorks MATLAB environment to big data (Big data) analytics and machine learning. Most HPC systems are built as computing clusters, consisting of many separate servers connected to a fast computer network (such as Infiniband). The HPC cluster is suitable for both parallel computing, ensuring the parallel execution of one large task, and distributive computing, performing many independent tasks on individual servers or processors. |
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Unaided Work
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
Off site
|
E-Studies platform
|
1
|
Topics
|
HPC
Description
HPC (from English High performance computing) allows you to solve complex computing tasks in less time. Computing occurs on computers (shared memory mainframe, computing cluster) made up of many parallel processors. Today, HPC is an integral part of any modern university and is used for a variety of needs – from engineering tasks in the MathWorks MATLAB environment to big data (Big data) analytics and machine learning. Most HPC systems are built as computing clusters, consisting of many separate servers connected to a fast computer network (such as Infiniband). The HPC cluster is suitable for both parallel computing, ensuring the parallel execution of one large task, and distributive computing, performing many independent tasks on individual servers or processors. |
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Shanmugamani R. 2020. Deep Learning for Computer VisionSuitable for English stream
2.
Forsyth D.A. un Ponce J. 2021. Computer Vision: A Modern ApproachSuitable for English stream
3.
Rothman D. 2021. Transformers for Natural Language ProcessingSuitable for English stream
4.
Tunstall L., Leandro von Werra, and Wolf T. 2022. Natural Language Processing with TransformersSuitable for English stream
Additional Reading
1.
Sahu S.K., Balvir S.U., Sahu A.K., et.al. 2025. Advancing High Performance Computing for AI in the Era of Large-Scale Models A Research Roadmap. Parallel and High-Performance ComputiSuitable for English stream
2.
Juglan K.C., Sharma B., Gehlot A., et.al. 2023. Exploring the Effectiveness of Natural Language Processing in Customer Service. 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023, pp. 814 - 818Suitable for English stream