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Discrete Choice Analysis
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Related lectures (56)
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Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.
Model Evaluation
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Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
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.
Logistic Regression: Probability Modeling
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Covers logistic regression for binary classification using probability modeling and optimization methods.
Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion
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Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.
Principal Component Analysis: Dimension Reduction
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Explores Principal Component Analysis for dimension reduction in datasets and its implications for supervised learning algorithms.
Deep Learning: Principles and Applications
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Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Image Classification: Decision Trees & Random Forests
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Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation
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Explores over-learning, generalization, and under-learning in machine learning models.
Model Assessment: Metrics and Selection
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Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.
Machine Learning Applications: Regression and Classification
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Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Linear Regression: Basics and Gradient Descent
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Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
General Linear Model: Model Selection
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Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Statistical Theory: Inference and Optimality
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Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
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Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.
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