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.