Generative AI: EPFL Project

Related lectures (30)

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.

Showcases a project predicting Bitcoin's price using Twitter and ML, achieving 60% accuracy.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Explores the significance of data models in scientific research and the challenges posed by big data.

Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Explores economy-wide material flow accounts, analyzing material flows in an economy and their environmental implications.

Covers privacy paradigms, data collection by companies, legal frameworks, trust in data providers, and the security vs. privacy debate.

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