Schița de curs
Introducere
Teoria probabilității, selecția modelului, teoria deciziei și a informațiilor
Distribuții de probabilitate
Modele liniare pentru regresie și clasificare
Neural Networks
Metode Kernel
Mașini cu miez rar
Modele grafice
Modele de amestec și EM
Inferență aproximativă
Metode de eșantionare
Variabile latente continue
Date secvențiale
Combinarea modelelor
Rezumat și Concluzie
Cerințe
- Înțelegerea statisticilor.
- Familiaritate cu calculul multivariat și algebra liniară de bază. .
- O oarecare experiență cu probabilitățile.
Audiență
- Analiști de date
- Studenți la doctorat, cercetători și practicieni
Mărturii (5)
Very flexible.
Frank Ueltzhöffer
Curs - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Curs - Neural Network in R
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Curs - Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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
Curs - 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.