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Weak supervision
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Deep Learning Modus Operandi
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Discretization: Methods and Techniques
Explores discretization methods, including equal width and equal frequency techniques, as well as x2 statistics for independence testing.
Machine Learning: Basics and Applications
Covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.
Introduction to Machine Learning
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
Unsupervised Learning: PCA & K-means
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Machine Learning: Types and Applications
Covers the types of machine learning, including supervised, unsupervised, and reinforcement learning.
Supervised Learning in Asset Pricing
Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Introduction to Machine Learning
Covers the basics of machine learning, focusing on supervised learning algorithms and real-world applications.
Model Evaluation
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Statistical Learning: Fundamentals
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.