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Data Science and Education at EPFL
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Related lectures (32)
Data Science for Engineers: Part 2
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Explores data manipulation, exploration, and visualization in data science projects using Python.
Introduction to Data Science
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Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Data Mining: Introduction
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Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Overview: Information Management
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Explores levels of data abstraction, model building, usage, and representation, and the utility of information systems for decision-making.
Data Science Innovations: Insights, Challenges, and Future
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Explores advancements in data science, focusing on fast insights, data variety, and real-time intelligent data systems.
Digital History and Digitized Press
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Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Deep Learning: Data, Models, and Challenges
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Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.
Unsupervised Learning: Dimensionality Reduction and Clustering
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Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.
Deep Learning: Principles and Applications
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Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Knowledge Representation: Semantics and Data Structures
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Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.
Machine Learning Basics
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
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