Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Delves into the impact of bias in machine learning models and the importance of evaluating potential harm in developing such systems.
Explores the Ethics and Law of AI, focusing on data ethics, bias, and social justice in AI systems.
Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.
Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
