Veidlapa Nr. M-3 (8)
Study Course Description
Artificial intelligence Project Management
Course Code
SZF_305
Branch of Science
Computer and information sciences
ECTS
3.00
Target Audience
Information and Communication Science; Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Māris Ancāns
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Kuldigas Street 9C, Riga, szf@rsu.lv
About Study Course
Objective
The aim of the course is to prepare students for efficient planning and management of AI projects in different sectors. The course will provide theoretical knowledge of AI technologies and project management principles, as well as provide practical experience working with companies. Students will develop skills in teamwork, resource management and problem solving necessary for successful implementation of AI projects. When completing the course, students will be prepared to apply the acquired knowledge and manage AI projects in real business scenarios.
Preliminary Knowledge
Prior experience in data processing and programming is desirable. Knowledge of AI or the fundamentals of machine learning is desirable but optional.
Learning Outcomes
Knowledge
1.• AI/ML project lifecycle: ability to identify and explain key phases of AI/ML projects, from defining a problem to implementing a model.
2.• Data processing: knowledge of identifying data sources, cleaning and preparing data for model development.
3.• Designing and training models: know different ML algorithms, how to train them, and adjust hyperparameters.
4.• Model evaluation and monitoring: ability to assess and monitor models to ensure long-term performance and prevent model bias.
5.• Experimentation: understand the cycle of experiments in AI/ML projects and its differences from traditional development.
Skills
1.• Model evaluation and monitoring: interpret, monitor, evaluate model performance and abnormality mitigation.
2.• Monitoring data analysis: evaluate data preparation/cleaning and analysis processes for high quality results.
3.• Ready-to-use platforms: use existing platforms to speed up development.
4.• Communication: explain technical processes and results clearly and efficiently to team members and stakeholders.
5.• Troubleshooting: identify and resolve issues related to AI project management without direct involvement in technical execution.
Competences
1.• Strategic thinking: the ability to understand and develop long-term data and AI strategies aligned with the organization’s goals.
2.• Data-driven decision-making: the ability to make informed decisions based on AI model results and data analysis.
3.• Project management: competence in AI/ML project planning and coordination from concept to implementation.
4.• Team management: understanding the roles and abilities of team members.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Independent work |
-
|
10 points
|
|
Group work: students in groups will take roles (project manager, data engineer, data scientist, etc.) and work on solving the problem by following the lifecycle of the AI/ML project. Define data sources, identify problems, and develop solutions. This group work can start at the beginning of lectures and will be presented at the end of the course. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Test |
-
|
10 points
|
|
Presentation of group work in the last lecture (40%) 2 quiz tests during lectures (30%) 2 lecture-based essays (30%) |
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Machine learning project lifecycle
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Assessment of project feasibility and risk analysis
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Team Dynamics in AI projects
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
AI project planning and resource allocation
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data strategy and management in AI projects
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Design and iteration of experiments
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Build robust ML data flow
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Optimisation and expansion of artificial intelligence systems
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Quick development of MVPs
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Study room
|
2
|
Topics
|
Guest interview: errors in AI projects
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Measuring success
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Group project job presentations
|
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Andy Pardoe. Confident AI, 2024Suitable for English stream
2.
Andriy Burkov. Machine Learning Engineering, 2020Suitable for English stream
Additional Reading
1.
Tyagi L., Gupta A., Sisodia V.S. Revolutionizing Industries: AI-Driven Case Studies and Success Stories in Real-World Applications and Innovations. 2024. Proceedings of International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI
2.
Álvarez López J.A. Case Studies of Real AI Applications. 2022. Artificial Intelligence for Business: Innovation, Tools and Practices, pp. 141 - 157Suitable for English stream
3.
Gabsi A.E.H. Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review. 2024. Annals of Operations ResearchSuitable for English stream
4.
Rashid A.B., Kausik M.A.K. AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. 2024. Hybrid Advances, 7, art. no. 100277Suitable for English stream