5 machine learning trends that will define 2017

Machine learning is at the core of many innovations that are set to improve our daily lives this year.



1.     Machine learning in finance

The finance industry has historically used machine learning in consumer services such as credit checking and fraud investigation. But recently, with more accessible computing power and open source tools, the financial sector is using machine learning in applications ranging from loan approval and risk assessments, to asset management.

A recent advancement called sentiment analysis involves considering the impact of social media and news trends on commodities prices.

To learn more: 5 ML trends that will define 2017


Artificial intelligence is changing the face of finance


For better or worse, artificial intelligence (AI) has long been hailed as a symbol of the future. However, in many ways, the age of AI is already upon us. From Google to gaming, early versions of the technology are becoming more readily integrated into our daily lives. It is therefore no great surprise that AI has also found its way into an area where the speed and accuracy of digital calculations by a computer can far outweigh anything a human can do: financial trading.



To know more about it, Artificial intelligence is changing the face of finance

Fintech: Search for a super-algo

ai There has been a missuse or misundestanding about what Machine learning, a branch of AI, is. Generally,  many people relates the term AI to mean sentient computers as the ones in SciFi movies, but  in practice everyday tools such as Google’s language translation service, Netflix’s film recommendation engine or Apple’s Siri virtual assistant deploy rudimentary forms of AI.


In fact, an interesting view of ML and AI is posed by Matthew Dixon, assistant professor of finance at the Illinois Institute of Technology, who defines machine learning as an “optimisation machine that minimises chaos”. It can learn the difference between bananas and apples and sort them out, or even teach a computer how to play and quickly master a game like Super Mario from scratch. Machine learning can also be unleashed on “unstructured data”, such as jumbled numbers but also images and videos that are usually difficult for a computer to understand.
To learn more about this topic, visit Fintech article in the FT

Machine Learning for Trading

Level: Intermediate

Hours: 6hs per week . Aprox. 4 months

Built by Georgia Tech


Course Description

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

To learn more about it, visit  Machine Learning for Trading

Algorithms are changing business: here’s how to leverage them

 algoritFrom data to algorithms

With the advance of technology, companies and consumers are generating more and more data. Collecting and storing massive amounts of data is not enough to gain competitive advantage. In order to beat the competition, organisations must do more than simply analyse the data. It’s now about what actions you can derive from your data in order to add value. Bring in the algorithms…



Read more: Algorithms are changing business


Diplomatura en Análisis de Datos para Negocios, Finanzas e Investigación de Mercados

Inicio:  Saturday, MAY 7, 2016  diplo


81 horas reloj


Días y horarios:

7 de Mayo al 17 de Diciembre de 2016.

Sábados de 10:00 a 13:00 Hs.


Lugar de realización:

Sede Centro – San Juan 951.



  • Externos: 10 Cuotas de $ 1500 y matricula de $ 1000.
  •  Alumnos y comunidad UAI:   10 Cuotas de $ 1100 y matricula bonificada.



Sede Centro – San Juan 951. Tel.: 4300-2147

Dirigido a:

Personal de empresas en áreas relacionadas con: toma de decisiones, estrategias de negocios, salud, análisis de riesgos, fraudes, finanzas, marketing y medicina.

Estudiantes de nivel terciario, estudiantes de grado de cualquier carrera afín. Podemos mencionar entre otras: Ciencias Empresariales, Ingeniería en Sistemas, Marketing, Ciencias Políticas, Gestión en Salud, Ciencias Exactas, etc.

De la misma manera, se encuentra fuertemente dirigida a investigadores en diversas disciplinas.



Que el alumno:

  • Adquiera una amplia y profunda gama de conceptos del Data Mining.
  • Conozca casos de éxito en diferentes industrias.
  • Pueda aplicar los conceptos adquiridos vivenciando el potencial de dichos conceptos.
  • Pueda programar aplicaciones sencillas para resolver problemas particulares.
  • Pueda profundizar en las plataformas de Software Libre mencionadas  para trabajar con toda la potencialidad del Data Mining, tanto a nivel académico como laboral.
  • Pueda aplicar los conocimientos obtenidos en la resolución de problemas reales en su campo laboral.



A lo largo de la Diplomatura se expondrán los algoritmos más utilizados en el Análisis de Datos, enfocándose en sus aplicaciones prácticas pero dando un importante panorama de sus aspectos teóricos.
Se explorarán diversas bases de datos orientadas a problemas relacionados con diferentes negocios y crearán potentes modelos predictivos y descriptivos orientados específicamente a resolver problemas empresariales.

Entre las problemáticas más destacadas a analizar se encuentran:

  • Segmentación Avanzada de Clientes
  • Predicción de la Demanda
  • Modelos de Predicción para Series Temporales y Financieras
  • Modelos de Scoring
  • Análisis de Riesgo
  • Detección y Prevención de Fraudes

Entre los algoritmos más importantes a estudiar se encuentran:

  • Redes Neuronales
  • Árboles de Decisión
  • Inferencia Bayesiana
  • Algoritmo de clustering K-Means
  • Teoría de la Información

A cargo de:

Juan Pablo Braña

Dra. Cristina Camós

Trad. Prof. Alejandra Litterio

Ing.Alexis Sarghel


Consultas a:


R coming to Visual Studio

R Tools for Visual Studio (RTVS) follows the model of Python Tools for Visual Studio: it’s an open-source plug-in to Visual Studio that makes it a complete IDE for R, with syntax-aware editing, a command-line REPL, and interactive debugging. (Like PTVS, there will be a GitHub repo for RTVS when it’s ready for release.)


Visual Studio R

Of course, RStudio already has excellent capabilities for developing R code, and RTVS isn’t available — yet. But if you already develop in Visual Studio, or want to develop R code alongside C++, JavaScript, Python or any language supported by Visual Studio, send an email to RTVS-Invite@Microsoft.com to sign up for early access to RTVS.

For more go to: Revolutionary Analytics

Facebook data collection and photo network visualization with Gephi and R

In the following tutorial Katherine Ognyanova explains how to collect and visualize data  from Facebook with R and Gephi,  using people’s profile photos as node images (three ways: with Gephi, igraph, or qgraph): Get the full R script here.

1.  Explaining Gephi

2. Collecting Facebook Data

3. Visualizing  the network with Gephi

4. Visualizing  the network with igraph

FB igraph

Static and dynamic network visualization with R

Katherine Ognyanova proposes  a detailed tutorial for network visualization.

1. Basics for Network visualization: goals, types, etc

2.Data format, size and preparation

3.Data set 1 and Data set 2

4. Network visualization with igraph

5. Plotting

Also the code and data can be dowloaded from:

Download this tutorial as a PDF file.

Download the example datasets and R code this tutorial uses.

The code and data are also available on GitHub.

network visualization

Read more at: http://kateto.net/network-visualization