Discrete choice and machine learning: two methodologies

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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Explores Gaussian mixture regression and overfitting with multiple Gauss functions.

Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Covers feature selection, kernel regression, and neural networks through exercises.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Covers exercises on regression functions using RLS, WLS, and LWR.

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.

Covers the basics of image processing using Fiji and various plugins.

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