Provides a detailed overview of machine learning with emphasis on practical application, covering data preparation techniques, statistics, regression, decision trees, neural networks, kernel machines, and unsupervised learning models.

Covers mathematical theories of signal processing with applied settings, focusing on analysis, optimization, and adjustment of data and signals.

Covers linguistic theories, NLP techniques, challenges, tools, and principles of generative AI models.

Applies machine learning and signal processing concepts to computer vision and visual content understanding.

Examines processing of streaming sensor data, including pre-processing, filtering, interpretation, and context extraction.

Explores hardware platforms, software tools, and techniques for embedded systems design, with focus on microcontroller applications.

Focuses on using FPGA architecture and algorithms for deep neural network learning and implementation across industries.

Covers implementation and execution of code across various platforms considering capabilities and limitations.

Final project applying comprehensive program knowledge to create smart sensor solutions, such as mobile sensor devices for biomedical applications.