Sela

Machine Learning and Artificial Neural Networks

Description
The 3-day long course is intended for the additional professional education and training of the hi-tech professionals working in a wide range of companies, where the intelligent data processing may apply and become the core value of the business. This includes web industry, robotics, autonomous cars, drones, IoT and smart homes, big data (including marketing analytics) companies, finances and insurance, etc. The course targets the most contemporary and market validated concepts in machine learning, with the major focus on artificial neural networks and related techniques. As the outcome, the students are expected to have the initial although solid knowledge of the basic principles of machine learning, key architectures, applications and tools they could then use in their professional activities.
Intended audience
It is expected that an average student has received 1st degree (B.Sc.) in the following and similar fields: Applied Mathematics, Computer Science, Electrical Engineering, Physics, Biophysics.

Topics

Can machine learn
What actually the machine learning means
Technical and biological background
Most popular trends
ANN as the blackbox model for the natural brain
Historical retrospective
Main models of ANNs
ANN math
Supervised learning
Unsupervised learning
Reinforcement learning
Backpropagation
Hebbian rule
Feed-forward nets
Convolutional neural networks (CNN)
Deep learning
Generative models
Machine vision (autonomous cars, robotics, security)
Unstructured data processing (web, corporate archives)
Natural language processing (voice assistance, chatbots, automated advisory)
Financial and retail applications (fraud identification and prevention, client personalization)
Google Tensor Flow
Microsoft CNTK
Amazon MXNet

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