Big Data: Challenges and Implications

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Deep Learning: Data, Models, and Challenges

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

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Diffusion-Convection: Modeling and Schemes

Covers modeling and numerical schemes for diffusion-convection problems.

Data Warehousing: Overview and Challenges

Introduces data warehousing fundamentals, challenges, and the innovative concept of a 'lakehouse'.

Representativeness, Reliability, and Contribution

Discusses data quality evaluation, reliability, representativeness, and process contribution in Life Cycle Assessment.

Block Pulled by a Spring: Dynamics

Covers the dynamics of a block connected to a spring, deriving equations of motion and solving for key time points.

Big Data: Processing and Dimensions

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