Concept
Loss function
Related lectures (123)
Maximum Likelihood Estimation: Econometrics
Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.
Linear Dimensionality Reduction: PCA and LDA
Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.
Projected Gradient Descent and Quadratic Penalty
Covers Projected Gradient Descent and Quadratic Penalty methods for optimization problems.
Bagging and Random Forests
Covers ensembling, bagging, random forests, variable importance, and OOB cross-validation in machine learning.
Proximal Operator and Gradient Descent
Covers proximal operators, gradient descent, optimality conditions, and convergence analysis in optimization problems.
Word Embeddings: Context and Representation
Explores word embeddings, emphasizing word-context relationships and low-dimensional representations.
Convergence Criteria
Explores convergence criteria in optimization algorithms, emphasizing the importance of stopping conditions and attention to large values.
Linear Regression: Basics and Applications
Introduces linear regression, from history to practical applications, including model building, prediction, and evaluation.
Risk Management: Quantitative Methods
Explores risk management concepts, including VaR, ES, and measurement methods.
Linear Models for Classification
Explores linear models for classification, logistic regression, and gradient descent in machine learning.