Category: Results & outputs

Information day in Italy – 16th February 2022

The Info Day in Italy is held by ISTC-CNR on Wednesday 16 February, from 15:00 to 19:00 as a virtual event (registration on Eventbrite)

The results of the project are presented by Gianluca Baldassarre, research director at ISTC-CNR, and president of the Advanced School in AI (AS-AI)

Representatives from the professional training sector (EULAB Consulting, ITS GALILEI-SANI, MAGISTRA GROUP, ITALIA CAMP), AI companies (INGLOBE TECHNOLOGIES) and sector experts (ISTAT) participated in the networking event.

The Info Day  included a “hands-on” session centered on the training modules of the ARIS course.

38 participants followed the meeting.

At the following link the detailed agenda.

ARIS Information Day in Barcelona– 16th February 2022

17 participants attended online and 38 participants attended in person at
the ARIS National Information Day, organised by the Department of Computer Science on February 16th 2022.

During the Info-day of the ARIS project, Karina Gibert, professor at the EIO department and director of the IDEAI-UPC research centre, presented a general review of the dizzying development of Artificial Intelligence from a perspective ethics

Javier Larrosa presented ARIS project MOOC.

Presentation on Ethical Aspects of AI you can see on Youtube platform.

ARIS Info-Day Vilnius

We are pleased to invite you to a ½ day presentation on ARIS, a MOOC about AI skills for ICT professionals financed by Erasmus+ and co-developed by two well-recognized universities providing AI courses, UPC (Universitad Politechnica de Catalunya) and ISTC (Institute of Cognitive Science and Technologies).

During the event, three speakers will introduce you to the artificial intelligence course content and its teaching materials, review trends in artificial intelligence and deep learning, and present information on the relationship between robot humanoids and artificial intelligence.

The event will occur at Vilnius University, Faculty of Informatics and Mathematics, Naugarduko str., 24 auditory 102 on Thursday 17th February at 9:00 AM. The agenda of the event is here.

The event is free, and registration for the event takes place 30 minutes before the presentations start. More information about the online artificial intelligence course and its materials can be found here:

https://www.openlearning.com/courses/artificial-intelligence-ai-skills-for-ict-professionals/?cl=1

https://aris-project.eu/results-outputs/aris-learning-units

Pilot delivery of the ARIS online course​

The ARIS curriculum was pilot delivered online from 4 October to 30 November 2021, with the actual participation of target groups, and overarching purpose to evaluate the educational value of the developed learning materials. In total, 172 students enrolled in the ARIS MOOC, attending learning materials of 120-160 hours duration. Overall, the process recorded positive attitudes and comments on the educational value, comprehensiveness and usefulness of the ARIS curriculum and resources. Participants also provided valuable feedback for improving learning materials and fine-tuning the online course. The attached report summarises the main findings and the evaluation results from the pilot delivery of the ARIS curriculum.

Download the report here.

Package of training and assessment materials in Artificial Intelligence

├─ Learning Unit 1 – Foundations of Artificial Intelligence.
│├ Lesson 1 – Scope of AI (document, slides).
│├ Lesson 2 – Problem Solving (document, slides).
│├ Lesson 3 – Knowledge Representation (document, slides).
│├ Lesson 4 – Machine Learning (document, slides).
│├ Lesson 5 – Applications (document, slides).
│└ Lesson 6 – Ethical Implications (document, slides).
│
├─ Learning Unit 2 – Machine Learning.
│├ Lesson 1 – Introduction to Machine Learning (document, slides).
│├ Lesson 2 – Languages and Resources (document, slides).
│├ Lesson 3 – Data Transformation and Visualization (document, slides).
│├ Lesson 4 – Supervised Linear Machine Learning (document, slides).
│├ Lesson 5 – Supervised non linear Machine Learning (document, slides).
│└ Lesson 6 – Unsupervised Machine Learning (document, slides).
│
├─ Learning Unit 3 – Neural Networks and Deep Learning.
│├ Lesson 1 – Brain Origins and Elements of Artificial Neural Networks (document, slides).
│├ Lesson 2 – Simple Perceptrons and Supervised Learning (document, slides).
│├ Lesson 3 – Multilayer Perceptron and Keras (document, slides).
│├ Lesson 4 – Deep Learning for Image Classification, Convolutive Neural Networks (document, slides). 
│├ Lesson 5 – Different CNNs for Image Classification (document, slides).
│└ Lesson 6 – Real Time Objects Locatization with YOLO (document, slides).
│
└─Learning Unit 4 – AI for Solving real-life problems.
   ├ Lesson 1 – Natural Language Processing Introduction, Embeddings Classification (document, slides).
   ├ Lesson 2 – Neural Networks for NLP and Libraries (document, slides).
   ├ Lesson 3 – New Approaches, Applications and Open Problems (document, slides).
   ├ Lesson 4 – BigData Problems, Core Techniques and Introduction to Problems, Hadoop Spark (document, slides).
   ├ Lesson 5 – Spark for Big Data Processing (document, slides).
   └ Lesson 6 – Cloud Computing and ML with PySpark (document, slides).