Valencian Summer School in Machine Learning

September 13-14, 2018, Valencia, Spain

Machine Learning is enabling a transformation in the software industry without precedents. New Machine Learning powered predictive applications are performing jobs that were previously considered exclusive to highly skilled humans. We are already witnessing a new wave of innovation that is changing the face of all sectors of the economy.

BigML is bringing the fourth edition of our Summer School in Machine Learning to Valencia. We will hold a two-day crash course ideal for business leaders, industry practitioners, advanced undergraduates, as well as graduate students, seeking a quick, practical, and hands-on introduction to Machine Learning to solve real-world problems. This Machine Learning School will serve as a good introduction to the kind of work that students can expect if they enroll in advanced Machine Learning masters. The venue will be confirmed shortly.

Comment about the Machine Learning School using #VSSML18

Lecturers

Charles Parker, Ph.D.

VP of Machine Learning Algorithms

Poul Petersen, M.Sc.

Chief Infrastructure Officer

Mercè Martín Prats, Ph.D.

VP of Insights and Applications

José Antonio Ortega (jao), Ph.D.

Co-Founder, Hacker at Large, and CTO

Schedule of Lectures

The goal of this Summer School is to introduce basic as well as more advanced Machine Learning concepts and techniques that will help you boost your productivity significantly. All lectures will take place from 8:30 AM to 6:30 PM CEST during September 13 and 14, 2018, and there will parallel sessions for the audience to attend their preferred option. The venue will be announced soon.

Day 1

08:30 AM - 09:00 AM
Reception, Breakfast, and Networking
09:00 AM - 09:15 AM
Opening Remarks
09:15 AM - 10:45 AM
Introduction, Models, and Evaluations
09:15 AM - 10:45 AM
Basic Data Transformations
10:45 AM - 11:15 AM
Coffee Break and Networking
11:15 AM - 12:45 PM
Ensembles and Logistic Regressions
12:45 PM - 01:00 PM
Summary of Morning Sessions
11:15 AM - 12:45 PM
Feature Engineering
12:45 PM - 01:00 PM
Summary of Morning Sessions
01:00 PM - 02:00 PM
Lunch and Networking
02:00 PM - 03:30 PM
Deepnets and Time Series
02:00 PM - 03:30 PM
REST API and Bindings
03:30 PM - 04:00 PM
Coffee Break and Networking
04:00 PM - 05:00 PM
Building your Own Supervised Models - Part I
05:00 PM - 06:00 PM
Building your Own Supervised Models - Part II
04:00 PM - 05:00 PM
Examples of Basic Data Transformations and Feature Engineering
05:00 PM - 06:00 PM
Examples with the BigML REST API and Bindings
06:00 PM - 06:30 PM
Case Study #1
06:00 PM - 06:30 PM
Case Study #1

Day 2

08:30 AM - 09:00 AM
Breakfast and Networking
09:00 AM - 09:15 AM
Opening Remarks
09:15 AM - 10:45 AM
Clusters and Anomaly Detection
09:15 AM - 10:45 AM
Basic ML Workflows and WhizzML
10:45 AM - 11:15 AM
Coffee Break and Networking
11:15 AM - 12:45 PM
Association Discovery and Latent Dirichlet Allocation
12:45 PM - 01:00 PM
Summary of Morning Sessions
11:15 AM - 12:45 PM
Advanced ML Workflows: Feature Selection, and Boosting
12:45 PM - 01:00 PM
Summary of Morning Sessions
01:00 PM - 02:00 PM
Lunch and Networking
02:00 PM - 03:30 PM
Building your Own Unsupervised
Models
02:00 PM - 03:30 PM
Advanced ML Workflows: Gradient Descent, and Stacking
03:30 PM - 04:00 PM
Coffee Break and Networking
04:00 PM - 04:45 PM
Case Study #2
04:45 PM - 05:30 PM
Case Study #3
05:30 PM - 06:15 PM
Case Study #4
04:00 PM - 04:45 PM
Examples of Basic ML Workflows
04:45 PM - 05:30 PM
Examples of Advanced ML Workflows
05:30 PM - 06:15 PM
Case Study #2
06:15 PM - 06:30 PM
Closing Remarks
06:30 PM - 07:00 PM
Drinks with the Lecturers

Register Here

Please fill in this form to join the VSSML18 and we will send you the invitation to purchase your ticket shortly. The ticket for this two-day event is 65€ per person, and it is intended to help cover food and drinks for the two days: breakfasts, lunch breaks, several coffee breaks, drinks, and snacks.

Co-organized by:

BigML Ayuntamiento de Valencia Valenciactiva VIT emprende

In collaboration with: