Course Outline
Introduction
Probability Theory, Model Selection, Decision and Information Theory
Probability Distributions
Linear Models for Regression and Classification
Neural Networks
Kernel Methods
Sparse Kernel Machines
Graphical Models
Mixture Models and EM
Approximate Inference
Sampling Methods
Continuous Latent Variables
Sequential Data
Combining Models
Summary and Conclusion
Requirements
- Understanding of statistics.
- Familiarity with multivariate calculus and basic linear algebra.
- Some experience with probabilities.
Audience
- Data analysts
- PhD students, researchers and practitioners
Testimonials (5)
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course - 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
Course - 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.