The Conference for Machine Learning Innovation

Machine Learning: The Bare Math Behind Libraries

Session
Join the ML Revolution!
Register until April 30:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until April 30:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until May 28:
✓ ML Intro Day for free
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $580
Register Now
Join the ML Revolution!
Register until May 28:
✓ ML Intro Day for free
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

Machine learning is one of the hottest buzzwords in technology today as well as one of the most innovative fields in computer science – yet people use libraries as black boxes without basic knowledge of the field. In this session, we will strip them to bare math, so next time you use a machine learning library, you’ll have a deeper understanding of what lies underneath.

During this session, we will first provide a short history of machine learning and an overview of two basic teaching techniques: supervised and unsupervised learning.

We will start by defining what machine learning is and equip you with an intuition of how it works. We will then explain gradient descent algorithm with the use of simple linear regression to give you an even deeper understanding of this learning method. Then we will project it to supervised neural networks training.

Within unsupervised learning, you will become familiar with Hebb’s learning and learning with concurrency (winner takes all and winner takes most algorithms). We will use Octave for examples in this session; however, you can use your favorite technology to implement presented ideas.

Take me to the full program of Zum vollständigen Programm von Munich Munich .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von MunichMunich program. Take me to the current program of . Hier geht es zum aktuellen Programm von Munich Munich , Singapore Singapore or oder Berlin Berlin .

Behind the Tracks