Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.P.3.00

Description Period: 2025./26 academic year pavasara semestris

Study Course Accepted: -

Study Course Description

Basics of Artificial Intelligence and Machine Learning

Main Study Course Information

Course Code
SZF_167
Branch of Science
Electrical engineering, Electronic engineering, Information engineering; Other Sub-Branches of Electrical Engineering, Electronics, Information and Communication Technology
ECTS
5.00
Target Audience
Management Science
LQF
Level 7
Study Type And Form
Full-Time; Part-Time

Study Course Implementer

Course Supervisor
Nataliia Kinash
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts

RSU Department of Social Sciences, Kuldīgas street 9c, szf@rsu.lv

About Study Course

Objective

The student will learn to apply AI/ML techniques to solve business problems, perform data analysis, build predictive models, and make data-driven decisions while ensuring ethical AI use.

Preliminary Knowledge

No prior experience in AI or machine learning is required. However, a basic understanding of math, especially linear algebra, statistics, and probability, is helpful. Familiarity with Python programming (e.g., variables, loops, functions) and working with structured data (like spreadsheets or CSV files) will support course engagement. Strong analytical skills and an interest in problem-solving are also beneficial.

E.g.:

https://www.udacity.com/course/introduction-to-python--ud1110

https://www.udacity.com/course/intro-to-statistics--st101

Learning Outcomes

Knowledge

1.Explain key algorithms in supervised and unsupervised learning, including regression, classification, clustering, and ensemble methods.

Individual work and tests

Individual work • Literature studies

2.Describe and interpret basic model evaluation metrics (e.g., accuracy, precision, recall) and analyze model performance results.

Individual work and tests

Literature studies • Individual work

3.Describe the core functionalities of open-source AI/ML tools and libraries (e.g., scikit-learn).

Individual work and tests

Individual work • Literature studies

Skills

1.Perform exploratory and visual data analysis and basic data preprocessing techniques.

Individual work and tests

Independent assignments

2.Recognize the use-case for the key algorithms in supervised and unsupervised learning, including regression, classification, clustering, and ensemble methods.

Individual work and tests

Independent assignments

3.Build and evaluate machine learning models.

Individual work and tests

Independent assignments

4.Deploy basic models.

Individual work and tests

Independent assignments

Competences

1.Interpret model outputs and metrics to make data-driven business decisions.

Individual work and tests

Presentation of a ML solution

2.Integrate AI/ML models business workflows, aligning technological capabilities with organizational goals.

Individual work and tests

Presentation of a ML solution

3.Address ethical considerations, such as bias and fairness, when applying AI/ML in business environments.

Individual work and tests

Presentation of a ML solution

Assessment

Individual work

Title
% from total grade
Grade
1.

Individual work
15.00% from total grade
10 points

After each lecture studens complete and online quiz assessing the understanding of theoretical material covered in the lecture.
2.

Literature studies
-
-

To prepare for lectures and practical classes students should read the assigned literature and watch the video materials available on the Moodle learning platform.

Examination

Title
% from total grade
Grade
1.

Independent assignments
35.00% from total grade
10 points

Short independent assignments completed during the course will be evaluated. The assignments consist of practical in-class exercises.
2.

Presentation of a ML solution
50.00% from total grade
10 points

In the final class, each student will present their project, demonstrating their understanding and practical application of key concepts. The project should be submitted in advance according to the schedule published on the e-larning platform (Moodle). The presentation will consist of 10 minute demonstration that can includes slides (if necessary), a working demo, and code. Students will demonstrate their understanding of the solution by answering questions from the lecturer and peers.

Study Course Theme Plan

FULL-TIME
Part 1

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to AI, ML, and Data-Driven Decision Making

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Exploratory and Visual Data Analysis for Business Insights

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Statistics and Data Preprocessing

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Feature Engineering and Feature Selection

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Predicting Outcomes with Linear Regression

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Binary Classification and Customer Segmentation with Logistic Regression

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Decision Trees

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ensemble Learning: Random Forests for Improved Decision-Making

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

k-Nearest Neighbors (k-NN) and Clustering

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Principal Component Analysis (PCA) for Reducing Data Complexity

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Boosting Methods (XGBoost)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Comparing Gradient Boosting Methods for Optimal Results

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Forecasting Trends with Time Series Analysis

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Model Deployment and Business Applications

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Automating Model Building with AutoML

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Efficient Data Handling for Big Data

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Monitoring for Ensuring Consistent Results

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Meta-Learning and Advanced AutoML Techniques

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ethics, Bias, and Fairness in Decision-Making with AI

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Evaluation and Course Wrap-Up
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to AI, ML, and Data-Driven Decision Making

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Exploratory and Visual Data Analysis for Business Insights

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Statistics and Data Preprocessing

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Feature Engineering and Feature Selection

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Predicting Outcomes with Linear Regression

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Binary Classification and Customer Segmentation with Logistic Regression

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Decision Trees

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ensemble Learning: Random Forests for Improved Decision-Making

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

k-Nearest Neighbors (k-NN) and Clustering

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Principal Component Analysis (PCA) for Reducing Data Complexity

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Boosting Methods (XGBoost)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Comparing Gradient Boosting Methods for Optimal Results

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Forecasting Trends with Time Series Analysis

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Model Deployment and Business Applications

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Automating Model Building with AutoML

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Efficient Data Handling for Big Data

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Monitoring for Ensuring Consistent Results

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Meta-Learning and Advanced AutoML Techniques

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ethics, Bias, and Fairness in Decision-Making with AI

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Evaluation and Course Wrap-Up
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam (Written)

Bibliography

Required Reading

1.

Géron, A. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow". 3rd ed., 2022.Suitable for English stream

2.

Bishop C. M., "Pattern Recognition and Machine Learning".

Additional Reading

1.

Theobald, O. "Machine Learning for Absolute Beginners: A Plain English Introduction"Suitable for English stream

2.

Provost, F., & Fawcett, T. "Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking"Suitable for English stream

3.

Knaflic, C. N. "Storytelling with Data"Suitable for English stream

4.

Hyndman R. J., Athanasopoulos G. "Forecasting: Principles and Practice"

5.

Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001)

6.

Chen, T., & Guestrin, C.XGBoost: A Scalable Tree Boosting System. KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785 - 794(2016)

7.

Breck E., Cai S., Nielsen E., Salib M. & Sculley D., The ML test score: A rubric for ML production readiness and technical debt reduction. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 1123-1132.

8.

Hutter F., Kotthoff L., Vanschoren J."Automated Machine Learing", 2019.

9.

Barocas S., Hardt M., Narayanan A. "Fairness and Machine Learning: Limitations and Opportunities", 2023.

10.

Chen, T., & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016, pp. 785–794.