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Related lectures (32)

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Gradient Descent: MNIST Dataset and Logistic Loss

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Machine Learning and Privacy

Delves into machine learning's impact on privacy, discussing attacks, vulnerabilities, and ethical considerations in data usage.

Deep Learning: Data Representations and Neural Networks

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

Kernel K-means Clustering

Explores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Semiparametric Inference for Missing-Not-at-Random Data

Explores semiparametric inference for missing-not-at-random data, addressing challenges in statistical analysis and proposing a doubly-robust estimator.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Interactions with Surface and Atmosphere: Remote Sensing Principles

Discusses the principles of remote sensing, focusing on interactions between electromagnetic radiation and the Earth's surface and atmosphere.