Veidlapa Nr. M-3 (8)
Study Course Description
Tools for AI and Data Science
Course Code
SZF_171
Branch of Science
-
ECTS
3.00
Target Audience
Business Management; Information and Communication Science; Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Artis Birziņš
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Kuldīgas iela 9c, Rīga
About Study Course
Objective
The course aims to provide students with the knowledge and practical skills to use the latest artificial intelligence (AI) and data science technologies and related tools in a business or organizational context.
By learning the selection and application of technologies and tools such as cognitive services, generative artificial intelligence platforms, edge computing and cloud deployment techniques for machine learning models, and DevOps, students will learn to organize an appropriate development environment, enable innovation, and improve operational efficiency, achieving the goals set by the organization in areas related to the application of AI and data science.
By combining theory and practice, the course will prepare future leaders who will know what tools and working methods to use to exploit the opportunities of technology.
Preliminary Knowledge
Basic knowledge of Python programming, fundamentals of data science and machine learning, students are familiar with data visualization methods. Previous experience with cloud platforms (e.g. AWS, Azure) is desirable, but not mandatory. This knowledge will help students master the artificial intelligence tools and methods covered in the course.
Learning Outcomes
Knowledge
1.Students will be able to distinguish, define, describe, and explain the importance and application of AI and data science technologies and related tools in the process of creating AI and data science solutions.
Skills
1.Students will be able to configure and start using tools related to AI and data science technology to use them in the development of new AI and data science solutions. Students will be able to choose appropriate tools based on information about the work goal to be achieved, the type of data and the risks.
Competences
1.Students will be able to analyze, evaluate, and present the alternatives, options, and applications of tools needed to create AI and data science solutions in an organizational context. Students will be able to analyze and explain the use of different tools within the development of a single AI or data science project, depending on the project workflow or phase.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
The following practical work and tests are planned.
1) Theoretical question test. 25% of the total score.
2) Planning of AI and data science tools and work environment organization in the company (group presentation)
A case study type task, in which, after familiarizing themselves with the task conditions and the achievable organizational goals, students (in groups) must evaluate and select suitable tool alternatives and explain the AI DevOps work organization approach based on the acquired material. The assessment will be guided by the compliance of the selected solution with the organizational goals and constraints specified in the case description. 25% of the total score.
3) Cognitive service pilot prototype
A task in which the student will individually demonstrate a prototype of a cognitive cloud service (e.g. computer vision, voice, chat, etc.) of their choice, thus becoming familiar with the tools related to the demonstration process. 25% of the total score.
4) Model training and inference in a cloud computing service
Using a trial or other version, students will train and infer a machine learning model to practically master the tools involved in this process. The assessment will be based on the confirmation of a successful outcome of the activity. 25% of the total grade
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Students' knowledge is assessed on a 10-point scale based on the results of assignments and tests. Each of the four assignments will account for 25% of the total grade. For more information on the assessment criteria, please see the "Independent Work" section.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Introduction, the origins of AI from computing technologies and big data
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Science and AI in Organizations - The Tool Landscape
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
DevOps in AI and data science solution development
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Cognitive services and their application in practical work
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Deep Learning and Neural Networks / Alternatively, guest lecturer or practical work time
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Edge computing
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Generative AI: Tools and Opportunities
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Creating and managing AI models in the cloud: from training to inference
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Creating and managing AI models in the cloud: from training to inference / Guest speaker
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Deploying AI models locally
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Risks and ethics in developing AI solutions
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Future trends and conclusion of AI and AI tools
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Wikipedia. 2024. Cloud Computing. Wikimedia FoundationSuitable for English stream
2.
Eric Anderson, Florian Zettelmeyer. 2022. Leading with AI and Analytics Build Your Data Science IQ to Drive Business Value. McGraw-Hill EducationSuitable for English stream
3.
Microsoft. 2024. Azure Machine Learning documentationSuitable for English stream
Additional Reading
1.
Microsoft. 2024. What is Edge Computing?Suitable for English stream
2.
Jupyter Team. 2024. Project Jupyter DocumentationSuitable for English stream
Other Information Sources
1.
Bob Swan. 2024. How to Build a Thriving AI Ecosystem with Lisa Su, CEO of AMDSuitable for English stream
2.
Kamrad Ahmed. 2024 AI and Data Scientist RoadmapSuitable for English stream
3.
Lex Fridman. 2024. Pieter Levels: Programming, Viral AI Startups, and Digital Nomad Life.Suitable for English stream
4.
NVDIA. 2024. NVIDIA CEO Jensen Huang Keynote at COMPUTEX 2024Suitable for English stream
5.
NVDIA. 2024. AI and The Next Computing Platforms With Jensen Huang and Mark ZuckerbergSuitable for English stream
6.
Warpdotdev. 2024. Easiest Way to Fine-Tune a LLM and Use It With OllamaSuitable for English stream