Data Analysis and Pattern Classification
- Providing Statistical Learning and Signal Processing tools to model great amounts of data, captured by specialized sensors, for automatic data classification or prediction;
- Analyzing data from different applicative areas (Environmental Sciences, Telecommunications, Genomics, e-health, Telemedicine, Biometrics, Economics, Banking, Insurance, Textual /Multimedia Data Mining, Geo-localization,…)
- Conceiving complex systems in several areas
Data Analysis and Pattern Classification (DATAPAC)
Télécom SudParis
Presentation
This Master of Science degree meets the needs of multi-sensor data processing stored in very large databases. In several applicative areas such as Telecommunications, Medical Imaging, Virtual Reality, Telemonitoring, Biometrics, Bioinformatics, Environmental Sciences, Banking, Insurance, Data Mining (textual, multimedia), great amounts of data are captured through specialized sensors and must be processed and further analyzed for different purposes related to the application. This Master degree is designed to provide Machine Learning tools allowing all types of real data analysis, from end to end: once raw data has been captured, it is often noisy or degraded and must be pre-processed, in order to remove the noise; then pertinent information (features) must be extracted and modeled through Machine Learning techniques for automatic classification or prediction purposes. Through an interdisciplinary approach, advanced knowledge in Signal Processing, statistical models, Machine Learning, Computer Vision, data fusion, and content-based indexing and retrieval in multimedia databases is given to future data scientists.
Typical Jobs
- Data Scientists
- R&D engineers
- Project managers
- PhD position in a research laboratory
Background
Good background in Applied Mathematics and Computer Science is required.
Semester 1 (Fall – Sept. to Feb.)
Core Courses
- Computer Science - 6 ECTS
- Probability and Statistics - 3 ECTS
- Effective Communication - 3 ECTS
Specific Courses
- Optimization Methods - 6 ECTS
- Application of Statistical Methods - 9 ECTS
- French - 3 ECTS
Semester 2 (Spring – Feb. to June)
- Pattern Recognition and Biometrics - 5 ECTS
- Signal Enhancement Methods - 6 ECTS
- Signal Processing and Statistical Data Analysis - 5 ECTS
- Conferences on ICT & Operational Systems - 4 ECTS
- Scientific Project - 8 ECTS
- French - 2 ECTS
Semester 3 (Fall – Sept. to Feb.)
-
Core Courses
- Pattern Recognition and Neural Networks - 6 ECTS
- Statistical Analysis of Real Data - 6 ECTS
Research Track (« Parcours Recherche »)
- Image Processing - 6 ECTS
- Case Study on Data Science I : Deep Learning - 3 ECTS
- Case Study on Data Science II: On Comparing Classifiers - 3 ECTS
- Research Project on Data Science - 3 ECTS
Professional Track (« Parcours Professionnel »)
- Databases and Information Retrieval - 6 ECTS
- Connected Objects: Principles and Sensors’ Reliability - 6 ECTS
- Knowledge of Enterprise - 3 ECTS
Semester 4 (Spring – Feb. to June)
- Qualitative Data Analysis - 3 ECTS
- Bayesian Networks and Hidden Markov Models - 3 ECTS
- Master Thesis - 24 ECTS
