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Lecture
EDIC Open House 2023
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Related lectures (52)
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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.
Machine Learning Fundamentals
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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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Document Analysis: Topic Modeling
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Neuro-symbolic Representations: Commonsense Knowledge & Reasoning
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Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Introduction to Machine Learning: Course Overview and Basics
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Behavioural Control in Animals and Robots
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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.
Differential Geometry: Parametric Curves & Surfaces
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Introduces the basics of differential geometry for parametric curves and surfaces, covering curvature, tangent vectors, and surface optimization.
Deep Learning Fundamentals
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Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Improving Models of the Ventral Visual Pathway
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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
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Introduction to Machine Learning: Basics and Examples
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Apache Spark Ecosystem: Basics and Operations
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Computer Vision: Historical Insights and Project Inspirations
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Explores the historical development of computer vision and inspires innovative project ideas.
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Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
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