Programa del Curso
Introducción
Teoría de la Probabilidad, Selección de Modelos, Teoría de la Decisión y de la Información
Distribuciones de probabilidad
Modelos lineales para regresión y clasificación
Neural Networks
Métodos del kernel
Máquinas de kernel dispersas
Modelos gráficos
Modelos de mezcla y EM
Inferencia aproximada
Métodos de muestreo
Variables latentes continuas
Datos secuenciales
Combinación de modelos
Resumen y conclusión
Requerimientos
- Comprensión de la estadística.
- Familiaridad con el cálculo multivariante y el álgebra lineal básica.
- Algo de experiencia con probabilidades.
Audiencia
- Analistas de datos
- Estudiantes de doctorado, investigadores y profesionales
Testimonios (4)
Very flexible.
Frank Ueltzhöffer
Curso - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Curso - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Curso - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.