Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Explores the EPFL Alice unit's role in shaping machine learning and AI in Europe, focusing on research advancements and collaboration within the AI community.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Explores the impact of machine learning in understanding human diseases, focusing on historical significance, natural products discovery, and challenges in designer drugs.
Explores provably beneficial AI, aligning AI goals with human preferences and behaviors, illustrating complexities through examples like image classification and fetching coffee.
