Training camps

Training camps Year 2019-2020

  • Poste Italiane July 20-22. Registration is now closed.

      Abstract:

A network uniting the country The Poste Italiane Group constitutes the largest service distribution network in Italy. Its activities range from letter and parcel delivery to financial and insurance services, payment systems and mobile telecommunications. With its 158-year history, a network of more than 12,800 post offices, a workforce of approximately 126 thousand, total financial assets of €536 billion and 35 million customers, Poste Italiane is an integral part of Italy’s economic, social and productive fabric, occupying an unparalleled position in the country in terms of size, recognisability, reach and customer loyalty. In February 2018, Poste Italiane launched its new five-year Strategic Plan, Deliver 2022, which aims to maximise the value of the distribution network and take advantage of the market opportunities offered by digital transformation. This will involve a reorganisation of the mail and parcels segment, the expansion of financial services, consolidation of our leadership in the insurance sector and the development of payment systems. The Plan envisages investment of €2.8 billion, focusing on innovation in order to assist citizens, businesses and the Public Administration through the transition to the digital economy and offering increasingly innovative services, in line with the highest market standards. In 2019, the Group’s activities involving the production and delivery of goods and services generated direct, indirect and induced impacts on the Italian economy amounting to approximately €12.5 billion in terms of GDP. An estimated total of 189 thousand workers were employed along the production chain, resulting in the distribution of income amounting to €7.5 billion to workers.

Challenge 1 - Object Detection on satellite imagery

The purpose of this activity is to implement and train a predictive model that uses computer vision techniques to perform detection and classification of objects (buildings) using satellite images in urban areas. Students will be provided with a training labeled dataset for some urban areas, in order to train the model to recognize the different types of buildings in the aerial photos. Once the model is trained they will use it to implement the following two tasks:

·         Object detection Task: assigned a study area, students will have to identify the zones in which there is likely to be a resident population, identifying residential buildings on the provided images

·         Objects Classification task: always in the same study area, they will have to identify the number and type of residential buildings, in order to estimate the resident population

Challenge 2 – Big data for Smart City

Poste Italiane wants to play an active role by contributing to shaping and designing the Smart City, the place where we all will live in the future inspired by principle like as sustinability. 

Poste Italiane’s mission is to contribute in making our country a better place, by promoting innovative solutions that leverage its own unique potential: from smart mobility and smart delivery to the Post Office of the future; from PA services to digital payments.

We believe that data are at the art of this change and the ability to gain insights from data could enable new services and also a new way of living for our customer and for citizens.

We want you to imagine and propose an innovative solution, or more integrated solutions, for Poste Italiane for the Smart City field, based both on the big data deriving from Poste Italiane's AS-IS assets and on the potential big data generated by further future innovative solutions.

The solution must be illustrated complete with a business case that highlights:

- Main Assumption and Rational

- Underlying technical logics

- Analysis of the economic feasibility and the main costs to be incurred

Required skills:

  • Wide knowledge of Python and NumPy stack (NumPy, SciPy, Pandas, Matplotlib)
  • Students should have a practical experience in using at least one of the neural network libraries such as Tensorflow, Keras and PyTorch
  • Basic knowledge of image processing libraries such as OpenCV (higly recommended) or Pillow.
  • Theoretical knowledge of modern computer vision techniques (CNN) with a particular focus on neural networks for object detection (RCNN, FCN, SSD, ResNet, YOLO)
  • Big Data analytics competence and technical competence in the definition of Big Data analytics platforms
  • Ability to define Business Cases and Case Analysis in relation to processed Big Data
  • Ability to use Design Thinking methodology applied to the context
 
  • Mykhaylo AndriIuka, Google Research, September 3-5, on "Building an image search engine"

    Abstract:

How could we build an automated system that can find a photograph in a family album or an online photo collection given just a textual description? In this course we will cover fundamental techniques in computer vision and natural language processing that will help us to address this question. The main aim of the course will be to enable students to build their own prototype of the image search engine, and participate in online Kaggle competition organized for the course participants. To aid the students in this mission we will review common methods for representing images and text with neural networks.

Specific techniques that the students will learn:
- image representation with convolutional neural networks (CNNs)
- building recurrent neural networks with LSTM and GRU units
- generating natural language image descriptions (=image captioning)
- representing words and sentences with vector embeddings (Word2Vec, GloVe, and BERT)

The course will assume that the students have solid knowledge of Python and numerical computation package NumPy. Knowledge of neural network libraries such as TensorFlow and PyTorch is highly recommended, but not strictly required. We will provide self-study materials for students to catch-up on these libraries.