University:
PG
Date:
February 2026
Expected Duration:
3–6 Months
Format:
Hybrid
Level:
Intermediate
Language of Instruction:
English
Registration Deadline:
16 February 2026
Price:
TBC
Course Overview
The course introduces fundamental paradigms of artificial intelligence and machine learning with applications to intelligent data analysis in renewable energy systems. Students learn about knowledge representation, Bayesian networks, supervised and unsupervised learning, neural networks (including recurrent and convolutional architectures), decision trees, time series processing, and deep learning methods. Practical sessions cover data preparation, preprocessing, normalization, augmentation, model training, hyperparameter tuning, and performance evaluation. Emphasis is placed on applying AI/ML techniques to engineering and energy-related datasets, with implementation in Python and Jupyter notebooks.
Main Goal
To provide students with theoretical foundations and practical skills in artificial intelligence and machine learning methods for intelligent data analysis, with applications in renewable energy systems.
Skills To Be Gained
After this course, you can
- Ability to prepare, preprocess, and normalize engineering datasets
- Competence in selecting, training, and tuning machine learning models
- Understanding of supervised, unsupervised, and deep learning paradigms
- Skills in evaluating and presenting results of AI/ML models for engineering applications
Practical Notes
Contact details: piotr.szczuko@pg.edu.pl
Requirements
- Basic programming in Python and familiarity with Jupyter notebooks.
Teaching And Assessment
Lectures (15h) and laboratories (15h). Assessment based on lab reports (50%) and written test (50%).