Objectives of Programme's Learning Outcomes

• Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Aquire new knowledge independently.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.

Learning Outcomes of UE

At the end of this UE, the student should have acquired theoretical knowledge and practical skills related to one of the major paradigms of AI: "deep learning". He/she should :
- know the major applications of artificial intelligence to natural language,
- know some of the most recent machine learning methods,
- be able to implement complex artificial neural networks
- know how to use generic software libraries for deep learning

Content of UE

The UE is composed of one learning activities that exposes:
- artificial intelligence through deep learning applied to the modeling of time sequences, and in particular to natural language processing (chatbots, machine translation, information extraction, etc.)

This learning activity will include practicals to acquire the theory.

More details about the content are provided in the ECTS sheet of the learning activity.

Prior Experience

Not applicable

Type of Assessment for UE in Q2

• Presentation and/or works
• Oral Examination
• Practical test
• Graded tests

Q2 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

• Presentation and/or works
• Oral examination
• Practical Test
• Graded tests

Q3 UE Assessment Comments

Not applicable