Natural Language Processing (NLP) is an artificial intelligence field. It is an interdisciplinary scientific domain, it involves linguistics, computer science, cognitive science, psycho linguistics, logic etc.

This module gives to the student an overview about the theoretic background as well as the technical solutions to different problems imposed in natural language processing.

This course provides Master-level Bioinformatics students with a comprehensive introduction to phylogenetics and phylogenomics. It develops both theoretical understanding and practical skills required to analyze evolutionary relationships using computational and statistical approaches.

The course begins with foundational concepts of evolution, common ancestry, and phylogenetic tree interpretation. Students are introduced to molecular evolution principles and sequence alignment methodologies, including pairwise and multiple sequence alignment strategies. Emphasis is placed on evolutionary models, mutation processes, and selection mechanisms.

Tree construction methods are studied in detail, covering distance-based approaches (UPGMA, Neighbor-Joining) and character-based approaches such as Maximum Parsimony, Maximum Likelihood, and Bayesian inference. Students also learn to assess tree reliability using bootstrapping techniques.

Computational aspects form a core component of the course. Students gain exposure to widely used phylogenetic software tools including MEGA, RAxML, IQ-TREE, and BEAST. The course introduces scripting and automation using Python (Biopython) and R (APE), along with high-performance computing strategies for large-scale datasets.

Phylogenomics is addressed through genome-scale evolutionary analysis, ortholog identification, comparative genomics, and gene family evolution. Applications extend to evolutionary medicine and functional genomics.

Additional topics include molecular clocks, divergence time estimation, and Bayesian approaches to molecular dating. Practical applications are explored through case studies in microbiology, epidemiology (eg, viral evolution), biodiversity, conservation, and forensic phylogenetics.

The course also discusses major computational challenges, big data scalability, emerging phylogenetic network methods, and integration with structural and systems biology.

Evaluation is based on continuous assessment and examinations.

Project planning is the overall process of defining the objectives, scope, tasks, necessary resources, budget and schedule of a project to ensure its success.

It is based on a process philosophy, namely the breakdown of work, task scheduling, resource allocation, risk identification, etc., resulting in the development of a provisional schedule visualised using projection, implementation and control tools such as PERT and Gantt charts.

MINISTERE DE L’ENSEIGNEMENT SUPERIEUR ET DE LA RECHERCHE SCIENTIFIQUE

DEPARTEMENT DE SCIENCE DE LA MATIERE

Année Universitaire : 2025-2026

Master 2 (M2) Chimie inorganique

Planning des Examens de Rattrapage du semestre S3

Salle « C »

1ère Année Tronc Commun Sciences & Technologie « LMD »

Planning des examens pour le semestre paire 2025-2026

MINISTERE DE L’ENSEIGNEMENT SUPERIEUR ET DE LA RECHERCHE SCIENTIFIQUE

DEPARTEMENT DE SCIENCE DE LA MATIERE

Année Universitaire : 2025-2026

Licence (L2) Chimie

Planning des Examens Finaux du semestre S3

Amphi A6