Lectures related to Unstructured data

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Overview: Information Management

Explores levels of data abstraction, model building, usage, and representation, and the utility of information systems for decision-making.

Data, big data, clouds and IoT

Explores data representation, databases, cloud computing, and challenges in the cloud environment.

Methods of Applied Topology: Complexity of Data

Explores the complexity of data in databases and methods for extracting information.

Stockage et transmission des données 1: introduction; besoin de structure

Discusses the importance of structuring data for efficient storage and retrieval.

Data Lakes: Structure and Optimization

Explores data lakes, data structure, and optimization for effective querying.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Mobilities through Big Data

Discusses the influence of Big Data on mobility planning and optimization, exploring its promises and limitations.

Graph Mining: Social Networks and Data Analysis

Explores graph mining in social networks, covering data analysis and community detection.

Big Data: Processing and Dimensions

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

Data Projects Overview

Covers data projects milestones, available datasets, project ideas, and examples.

Distributed Computing: Challenges and Solutions

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Digital Humanities and Digitized Press

Explores digitized press archives, text data processing challenges, methodologies for group work, and the societal impact of digital humanities.

Data Warehouses: Introduction and Challenges

Covers the introduction and challenges of data warehouses, including integrating data, managing metadata, and optimizing query performance.

Deep Learning: Data Representations and Neural Networks

Explores data representations, histograms, neural networks, and deep learning concepts.

Digital Turn in History: Culturomics and Data Visualization

Delves into culturomics, a computational approach to studying cultural trends through quantitative analysis of digitized textual data.

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Semantic Web: Knowledge Graphs

Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.

Deep Learning

Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.