This course covers the foundations of machine learning, artificial neural networks, deep learning models and natural language processing, with a strong focus on their applications within educational contexts. Students will develop a comprehensive understanding of various architectures and algorithms, with hands-on experience in implementing machine learning to address real-world educational issues, such as personalized learning in different learning contexts (e.g., math, writing and etc.), student performance prediction, and multimodal learning analytics. In addition, students will develop skills to critically assess these approaches and examine their ethical and societal implications.

CILO-1: Describe the fundamental concepts of modern natural language processing and its applications in educational settings. CILO-2: Design and implement deep learning models using appropriate architectures for specific educational applications. CILO-3: Critically evaluate the effectiveness of machine learning-based solutions in addressing educational issues. CILO-4: Analyze and assess the ethical and societal implications of deploying machine learning approaches in educational settings.