Veidlapa Nr. M-3 (8)
Study Course Description
Artificial intelligence project management
Course Code
SZF_172
Branch of Science
Other social sciences
ECTS
6.00
Target Audience
Business Management; Health Management; Information and Communication Science; Management Science; Medical Technologies
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
Kuldīgas street 9c, Riga, szf@rsu.lv
About Study Course
Objective
The goal of the course is to prepare students for the effective planning and management of AI projects across various industries. The course will provide theoretical knowledge of AI technologies and project management principles, as well as practical experience through collaboration with companies. Students will develop skills in teamwork, resource management, and problem-solving necessary for successful AI project execution. Upon completing the course, students will be ready to apply their acquired knowledge and lead AI projects in real business scenarios.
Preliminary Knowledge
Previous experience in data processing and programming is preferred. Knowledge of artificial intelligence or machine learning fundamentals is preferred but not required.
Learning Outcomes
Knowledge
1.• AI/ML Project Lifecycle: Ability to identify and explain the key stages of AI/ML projects, from problem definition to model implementation. • Data Processing: Knowledge of identifying data sources, cleaning data, and preparing it for model development. • Model Development and Training: Familiarity with various ML algorithms, their training, and hyperparameter tuning. • Model Evaluation and Monitoring: Ability to evaluate and monitor models to ensure long-term performance and prevent model drift. • Experimentation: Understand the experiment cycle in AI/ML projects and its differences from traditional development.
Skills
1.• Model Evaluation and Monitoring: Interpret, monitor, evaluate models performance and drift mitigation. • Oversight of Data Analysis: Evaluate data preparation / cleaning, and analysis processes to ensure high-quality outputs. • Ready to go platforms: Use existing platforms to speed up the development. • Communication: Clearly and effectively explain technical processes and results to team members and stakeholders. • Problem Solving: Identify and resolve issues related to AI project management without direct involvement in technical execution.
Competences
1.• Strategic Thinking: Ability to understand and develop long-term data and AI strategies aligned with organizational goals. • Data-Driven Decision Making: Skill in making informed decisions based on AI model outcomes and data analysis. • Project Management: Competence in planning and coordinating AI/ML projects from concept to implementation. • Team Leadership: Understanding team member roles and abilities.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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Group Work: Students in groups will take on roles (project lead, data engineer, data scientist, etc.) and work on solving a problem, following the AI/ML project life cycle. They will define data sources, identify challenges, and develop solutions. This group work can begin in the early lectures and will be presented at the end of the course.
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Examination
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Title
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% from total grade
|
Grade
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|---|---|---|
|
1.
Examination |
-
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-
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Group work presentation at the last lecture (40%)
2 Quiz tests during lectures (30%)
2 Essays based on lectures (30%)
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
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Modality
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Location
|
Contact hours
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|---|---|---|
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On site
|
Auditorium
|
2
|
Topics
|
Optimizing and Scaling AI Systems for Production
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Rapid Development of MVP
|
-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
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On site
|
Auditorium
|
2
|
Topics
|
Measuring Success
|
-
Lecture
|
Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Strategy and Management for AI Projects
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-
Lecture
|
Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
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Topics
|
Guest Lecture: Pitfalls in AI projects
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
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Topics
|
Project Feasibility Assessment and Risk Analysis
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-
Class/Seminar
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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
|
Auditorium
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2
|
Topics
|
Guest Lecture: Pitfalls in AI projects
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
AI Project Planning and Resource Allocation
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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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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Team Dynamics in AI Projects
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-
Class/Seminar
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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
|
Auditorium
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2
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Topics
|
Experimentation Design and Iteration
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Optimizing and Scaling AI Systems for Production
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Building Robust ML Pipelines
|
-
Lecture
|
Modality
|
Location
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Contact hours
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|---|---|---|
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On site
|
Auditorium
|
2
|
Topics
|
Experimentation Design and Iteration
|
-
Lecture
|
Modality
|
Location
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Contact hours
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|---|---|---|
|
On site
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Auditorium
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2
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Topics
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Machine Learning Project Life-cycle
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Building Robust ML Pipelines
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Project Feasibility Assessment and Risk Analysis
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Strategy and Management for AI Projects
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Measuring Success
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Machine Learning Project Life-cycle
|
-
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
|
Rapid Development of MVP
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Group case work presentations
|
-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
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Topics
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Team Dynamics in AI Projects
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Total ECTS (Creditpoints):
6.00
Contact hours:
46 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, ICSCAISuitable for English stream
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