Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
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
Abhi always made sure we were following along. Good mix of practice and theory.
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Deep Reinforcement Learning with Python Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Really simple, easy to follow explanations Covered everything necessary in enough detail to understand fully, but so that it was not overwhelming good mix of theory and practice
Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Introduction to the use of neural networks Course
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Applied AI from Scratch in Python Course
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
Deep Reinforcement Learning with Python Course
Graphs in R :)))
Faculty of Economics and Business Zagreb
Neural Network in R Course
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Neural Network in R Course
new insights in deep machine learning
Josip Arneric
Neural Network in R Course
the interactive part, tailored to our specific needs
Thomas Stocker
Introduction to the use of neural networks Course
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
Introduction to the use of neural networks Course
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.
Jonathan Blease
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Related Courses
OpenNN: Implementing Neural Networks
14 hours
Pattern Matching
14 hours
Neural Network in R
14 hours
Applied Machine Learning
14 hours
From Zero to AI
35 hours
Microsoft Cognitive Toolkit 2.x
21 hours
PaddlePaddle
21 hours