Data Science and Education at EPFL

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Related lectures (32)

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Data Science in Personalized and Global Health: Privacy-Enhancing Technologies

Delves into data science in personalized and global health, emphasizing privacy-enhancing technologies and AI applications in healthcare.

Data Science Innovations: Insights, Challenges, and Future

Explores advancements in data science, focusing on fast insights, data variety, and real-time intelligent data systems.

Digital History and Digitized Press

Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Atomistic Machine Learning: Physics and Data

Explores Atomistic Machine Learning, integrating physical principles into models to predict molecular properties accurately.

Deep Learning: Data, Models, and Challenges

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

Big Data: Processing and Dimensions

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Excel Upgrade: Advanced Functions and Data Analysis

Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.