Training camps Academic Year 2017-2018

 

SAS and Facebook will offer to the Data Science students two Summer ​Training Camps
 
Training camp SAS will be held in the computer lab/room VII of the Department of Statistical Science on September 10-13.
 
Training camp Facebook will be held in the "Aula Magna" of the Department of Computer, Control, and Management Engineering  "Antonio Ruberti" on September 17-19
 
The Data Science students are required to take at least one of the two camps

Priority will be given to the sudents of the Academic Year 2017-2018. 

For students of previous Academic Year, who have never attended any training camp: please send an email to datascience@i3s.uniroma1.it  in order to receive the form
However, participation will be extended as far as possible to all interested students. 
 
 

Training camp SAS

Registration for training camps is over. Students will receive an email by September 6th to confirm enrollment

September, 10 - 13, 2018
Department of Statistical Science - Lab VII (ground floor)

Day1- 2-3: mon-Tue - Wed Sept. 10-11-12 (9:30/16:30)
Applied Analytics Using SAS Enterprise Miner

Hands-on training for SAS Enterprise Miner software

Topics:.

  • Introduction
  • Accessing & Assaying Prepared Data
  • Predictive Modeling Fundamentals & Decision Trees
  • Regressions
  • Neural Networks & Other Modeling Tools
  • Model Assessment
  • Model Implementation
  • Introduction to Pattern Discovery
  • Case Studies & Special Topics

Course Description:

The course covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).


Day 4Thur Sept. 13 (9:30/13:00)


SAS Visual Data Mining and Machine Learning.

Topics:

  • Demo and description of a complete process of Artificial Intelligence using Machine Learning techniques focusing on the new SAS Visual Data Mining and Machine Learning solution.
  •  Examples of integration with open Python within SAS Platform Getting Started with SAS Visual Analytics

 

Training camp Facebook

Registration for training camps is over. Students will receive an email by September 10th to confirm enrollment

September 17-19
Department of Computer, Control, and Management Engineering  "Antonio Ruberti" - Aula magna (First floor)

This mini tutorial will introduce students to the main techniques used in Natural Language Processing to manipulate and extract knowledge from natural language. We will present numerous examples using the popular framework Pytorch and we will do lots of examples that will be useful in real life applications.

Preliminary program: 
September 17th,  09:00 - 17:00 
  • Introduction to Machine Learning
    • Regression vs. Classification
    • Parameters Estimation
    • Pytorch
      • an example: logistic regression in Pytorch
  • Neural Networks
    • Perceptron
      • an example: Perceptrons in Pytorch
    • Multi output perceptrons == Feed Forward Neural Networks
    • Backpropagation
September 18th,  09:00 - 17:00 
  • Input Output Representation
    • One-Hot Encoding
    • Embeddings
    • An example: word2vec
  • Neural Network Architectures for NLP
    • MLPs (Multi Layers Perceptrons)
    • Convolutional Neural Networks (CNNs)
    • RNNs
      • GRUs
      • LSTMs
      • Attention Mechanisms
September 19th,  09:00 - 17:00 
  • Assignments
    • FastText
    • Seq2Seq
    • QueryEmbeddings
 
IMPORTANT: The students are required to bring a laptop with  pytorch  (https://pytorch.org/) and jupyter (http://jupyter.org/) installed. 

 

SAS and Facebook will offer to the Data Science students two Summer ​Training Camps
 
Training camp SAS will be held in the computer lab/room VII of the Department of Statistical Science on September 10-13.
 
Training camp Facebook will be held in the "Aula Magna" of the Department of Computer, Control, and Management Engineering  "Antonio Ruberti" on September 17-19
 
The Data Science students are required to take at least one of the two camps

Priority will be given to the sudents of the Academic Year 2017-2018. 

For students of previous Academic Year, who have never attended any training camp: please send an email to datascience@i3s.uniroma1.it  in order to receive the form
However, participation will be extended as far as possible to all interested students. 
 
 

Training camp SAS

Registration for training camps is over. Students will receive an email by September 6th to confirm enrollment

September, 10 - 13, 2018
Department of Statistical Science - Lab VII (ground floor)

Day1- 2-3: mon-Tue - Wed Sept. 10-11-12 (9:30/16:30)
Applied Analytics Using SAS Enterprise Miner

Hands-on training for SAS Enterprise Miner software

Topics:.

  • Introduction
  • Accessing & Assaying Prepared Data
  • Predictive Modeling Fundamentals & Decision Trees
  • Regressions
  • Neural Networks & Other Modeling Tools
  • Model Assessment
  • Model Implementation
  • Introduction to Pattern Discovery
  • Case Studies & Special Topics

Course Description:

The course covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).


Day 4Thur Sept. 13 (9:30/13:00)


SAS Visual Data Mining and Machine Learning.

Topics:

  • Demo and description of a complete process of Artificial Intelligence using Machine Learning techniques focusing on the new SAS Visual Data Mining and Machine Learning solution.
  •  Examples of integration with open Python within SAS Platform Getting Started with SAS Visual Analytics

 

Training camp Facebook

Registration for training camps is over. Students will receive an email by September 10th to confirm enrollment

September 17-19
Department of Computer, Control, and Management Engineering  "Antonio Ruberti" - Aula magna (First floor)

This mini tutorial will introduce students to the main techniques used in Natural Language Processing to manipulate and extract knowledge from natural language. We will present numerous examples using the popular framework Pytorch and we will do lots of examples that will be useful in real life applications.

Preliminary program: 
September 17th,  09:00 - 17:00 
  • Introduction to Machine Learning
    • Regression vs. Classification
    • Parameters Estimation
    • Pytorch
      • an example: logistic regression in Pytorch
  • Neural Networks
    • Perceptron
      • an example: Perceptrons in Pytorch
    • Multi output perceptrons == Feed Forward Neural Networks
    • Backpropagation
September 18th,  09:00 - 17:00 
  • Input Output Representation
    • One-Hot Encoding
    • Embeddings
    • An example: word2vec
  • Neural Network Architectures for NLP
    • MLPs (Multi Layers Perceptrons)
    • Convolutional Neural Networks (CNNs)
    • RNNs
      • GRUs
      • LSTMs
      • Attention Mechanisms
September 19th,  09:00 - 17:00 
  • Assignments
    • FastText
    • Seq2Seq
    • QueryEmbeddings
 
IMPORTANT: The students are required to bring a laptop with  pytorch  (https://pytorch.org/) and jupyter (http://jupyter.org/) installed.