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Applied Machine Learning
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Related lectures (31)
Model Evaluation
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Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Decision Trees and Random Forests: Concepts and Applications
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Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.
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.
Model Selection: Evaluation and Generalization
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Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Machine Learning Fundamentals
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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Decision Trees: Classification
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Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
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.
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.
Dimensionality Reduction: PCA & t-SNE
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Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Machine Learning at the Atomic Scale
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Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.
Supervised Learning: Classification Algorithms
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Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
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