| Course label : | Advanced Machine Learning 1 - Natural Learning Processing |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S3_AM1 - Advanced machine learning 1 |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
NLP: definition, applications, evaluation, brief history NLP pipeline: tokenization, symbolic and neural processing Language modeling Embeddings/RNN/Attention/Transformers Unsupervised learning, scaling, transfer, and training stages (RLHF) Text encoding, efficiency issues, multimodality Agents, tool use, RAG Privacy, fairness, multilinguality, data annotation
Educational goals
After successfully taking this course, a student should be able to: Roughly understand and apply modern NLP models
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Project (min) 0 - 20 (max)
Knowledge Exam (min) – 0 – 20 (max)
Paper presentation, (min) 0 - 20 (max)
Passing grade 10/20
Online resources
Introduction to Natural Language Processing By Jacob Eisenstein
Pedagogy
English is the default language.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Preferably: machine learning and basic computer science, Python