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Related lectures (54)
Introduction to Machine Learning
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Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Statistics: Exploratory Data Analysis
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Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
Data Science for Engineers: Part 2
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Explores data manipulation, exploration, and visualization in data science projects using Python.
Unsupervised Learning: Clustering & Dimensionality Reduction
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Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods
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Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.
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Deep Learning: Principles and Applications
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Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Neural Networks Recap: Activation Functions
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Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Block Pulled by a Spring
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Explores the dynamics of a block pulled by a spring under various conditions.
Regression Trees and Ensemble Methods in Machine Learning
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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
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IT Security Challenges
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Explores IT security challenges, Object Storage systems, website management transition, network security deployment, and project portfolio management at EPFL.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
NumPy Arrays and Graphical Representations: Introduction
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Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
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By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Data Visualization: Techniques and Applications
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Explores data visualization techniques, design impact, and interactive applications for effective information communication.
Vertical Metronome: Equilibrium and Stability
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Explores equilibrium and stability of a vertical metronome system with springs.
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