Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.R.1.00

Description Period: 2025./26 academic year rudens 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
-
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

Not necessary.

Learning Outcomes

Knowledge

1.- key algorithms in supervised and unsupervised learning, including regression, classification, clustering, and ensemble methods; - basic understanding of model evaluation metrics and interpreting model results; - practical knowledge of open-source AI/ML tools and libraries.

Skills

1.- performing exploratory and visual data analysis and basic data preprocessing techniques; - building and evaluating machine learning models; - deploying basic models.

Competences

1.- interpreting model outputs and metrics to make data-driven business decisions; - integrating AI/ML models business workflows, aligning technological capabilities with organizational goals; - address ethical considerations, such as bias and fairness, when applying AI/ML in business environments.

Assessment

Individual work

Title
% from total grade
Grade
1.

Individual work
-
-
The assignments combining theoretical questions and practical exercises.

Examination

Title
% from total grade
Grade
1.

Examination
-
-
Short independent assignments completed during the course will be evaluated. The assignments combine theoretical questions and practical exercises. In the final class, students will defend their solutions, demonstrating their understanding and practical application of key concepts.

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.