Disorder Transform

Disorder Transform

Math

1. Theoretical minimum
2. Distribuições de probabilidade
3. Entropia e informação mútua
4. Maximum Likelihood Estimation
5. Decomposição de matrizes
6. Gradiente descendente
7. More on Gradients
8. Multiplicadores de Lagrange
9. Method of kernels
10. Integer Programming

Machine Learning

1. Bias and Variance
2. Understanding regularization
3. Laplace Approximation
4. Computing Hessian and Jacobian
5. Counter Factual Model
7. Introdução ao Manifold Learning
8. Manifold Learning - Multidimensional Scaling
9. Active Learning
10. Expectation-Maximization Algorithm - k-Means
11. Neural Network as an Universal Approximator
12. Variational AutoEncoder

Ranking Problems

1. How to Learn to Rank
2. Ranknet
3. Ranknet - Teacher-Student strategy

Reinforcement Learning

1. Beyond conventional machine learning
2. AB test and the regret
3. The Multi-Armed Bandits: Exploitation vs Exploration

Differential Geometry

1. Problems and solutions in differential geometry
2. Mapas, Curvas, superfícies e Variedades
3. Espaços Vetoriais
4. Álgebras
5. Topologia
6. Campos Tensoriais Métricos

Extra content

Definições
Lista de símbolos

Datasets

Ranking dataset - Teams

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How is Ranking different?

How is Ranking different?¶

How to Learn to Rank

By onimaru
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