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

Advanced Machine Learning

Main Study Course Information

Course Code
SZF_168
Branch of Science
-
ECTS
5.00
Target Audience
Business Management; 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

Kuldīgas 9c, Rīga

About Study Course

Objective

By the end of this course, students will learn to apply advanced machine learning techniques, including neural networks, LLMs, reinforcement learning, GANs, and autoencoders, to solve business problems. They will learn to build, deploy, monitor, and explain models, ensuring ethical use and strategic business impact.

Preliminary Knowledge

"Fundamentals of artificial intelligence and machine learning" course or equivalent

Learning Outcomes

Knowledge

1.- Understanding advanced machine learning concepts, models, and algorithms. - Familiarity with advanced neural network architectures(CNNs, RNNs, GANs, LLMs), their advantages and limitations. - Understanding Reinforcement Learning. - Knowledge of ethical considerations, bias, and fairness in AI. - Understanding of model deployment and monitoring processes.

Skills

1.- Build and fine-tune advanced machine learning models. - Use Natural Language Processing Techniques including prompt engineering. - Deploy machine learning models. - Use explainable AI techniques like SHAP and LIME for model interpretability.

Competences

1.- Choose and apply machine learning solutions to improve business decision-making and efficiency. - Communicate model results and insights effectively to non-technical stakeholders. - Ensure ethical AI practices are integrated. - Adapt machine learning models in dynamic environments.

Assessment

Individual work

Title
% from total grade
Grade
1.

Individual work
-
-
assignments and a final project

Examination

Title
% from total grade
Grade
1.

Examination
-
-
The course evaluation combines assignments and a final project to assess practical and theoretical understanding. Assignments focus on applying advanced ML techniques in business scenarios. The final project integrates multiple concepts, requiring students to solve a real-world business problem and present results.

Study Course Theme Plan

FULL-TIME
Part 1

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Advanced Machine Learning

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced Feature Engineering and Data Transformation. Model Evaluation and Selection.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Neural Networks

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Regularization and Optimization in Neural Networks

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Convolutional Neural Networks (CNNs) for Image Analysis

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced CNN Architectures and Transfer Learning

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Time Series Analysis with Recurrenct Neural Networks (RNNs and LSTMs)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Natural Language Processing with Transformers

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Large Language Models (LLMs)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Prompt Engineering for LLMs

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Generative Adversarial Networks (GANs) and Synthetic Data

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Autoencoders and Anomaly Detection

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Reinforcement Learning (RL)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced Reinforcement Learning (Policy-Based Methods)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Meta-Learning and Adaptive Models

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Explainable AI and Interpretability

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ethics and Responsible AI for Customer-Facing Algorithms

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Deployment Strategies and Business Integrations

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Monitoring and Maintenance

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Project Presentation and Wrap-up
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Advanced Machine Learning

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced Feature Engineering and Data Transformation. Model Evaluation and Selection.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Neural Networks

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Regularization and Optimization in Neural Networks

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Convolutional Neural Networks (CNNs) for Image Analysis

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced CNN Architectures and Transfer Learning

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Time Series Analysis with Recurrenct Neural Networks (RNNs and LSTMs)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Natural Language Processing with Transformers

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Large Language Models (LLMs)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Prompt Engineering for LLMs

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Generative Adversarial Networks (GANs) and Synthetic Data

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Autoencoders and Anomaly Detection

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Reinforcement Learning (RL)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Advanced Reinforcement Learning (Policy-Based Methods)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Meta-Learning and Adaptive Models

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Explainable AI and Interpretability

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Ethics and Responsible AI for Customer-Facing Algorithms

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Deployment Strategies and Business Integrations

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Model Monitoring and Maintenance

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Project Presentation and Wrap-up
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam (Written)

Bibliography

Required Reading

1.

Raschka, S., Liu, Y., Mirjalili, V. "Machine Learning with PyTorch and Scikit-Learn"Suitable for English stream

2.

Foster, D., "Generative Deep Learning"

Additional Reading

1.

Goodfellow, I., Bengio, Y., & Courville, A. "Deep Learning"Suitable for English stream

2.

Barocas, S., Hardt, M., & Narayanan, A. "Fairness and Machine Learning: Limitations and Opportunities"Suitable for English stream

3.

Molnar, C. "Interpretable Machine Learning"Suitable for English stream

4.

Géron, A. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow", 2nd EditionSuitable for English stream

5.

Sutton, R. S., & Barto, A. G. "Reinforcement Learning: An Introduction"Suitable for English stream

6.

Bishop, C. "Pattern Recognition and Machine Learning"Suitable for English stream

7.

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., Zhong, C. “Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges” (Statistics Surveys, 2021).

8.

Goodfellow, I. J., Pouget‑Abadie, J., Mirza, M., Xu, B., Warde‑Farley, D., Ozair, S., Courville, A., Bengio, Y. “Generative Adversarial Networks” (arXiv preprint, 2014).

9.

Iusztin, P., Labonne, M. "LLM Engineer's Handbook: Master the art of engineering large language models from concept to production"

10.

Noyan, M., Marafioti, A., Farré, M., Zohar, O. "Vision Language Models"

Other Information Sources

1.

The Transformers by Hugging Face (open-source, website and documentation)Suitable for English stream