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This information is indicative and can be subject to change. 
Statistical learning
Teacher:   Alain Celisse 

E-mail:  [email protected]
ECTS: 2.5
Evaluation: Project consisting in analyzing data by means of Python libraries 
Previsional Place and time:  

Prerequisites: Background in applied mathematics, probability theory and main concepts of statistics. 
Aim of the course: Reviewing the main learning strategies classically applied when trying to extract valuable information form a dataset. The lectures detail the mathematical background for all the reviewed learning strategies in order to avoid misleading conclusions.
 Syllabus: 
1. Linear regression and model selection:
(a) Statistical model
(b) Penalized criteria: AIC/BIC
(c) Cross-validation
2. Classification:
(a) Logistic regression
(b) Discriminant Analysis: linear and quadratic
(c) kNN classifier
3. Clustering by Gaussian mixture models:
(a) Gaussian mixture models
(b) EM algorithm
4. Visualisation/dimension reduction:
(a) Principal Component Analysis
(b) Spectral Clustering
5. Convex optimization and machine learning:
(a) Convex optimization
(b) KKT conditions and Duality gap
(c) Gradient Descent
(d) Stochastic Gradient Descent
6. Introduction to Deep Neural Networks:
(a) Multi-layer perceptron
(b) Back-propagation et SGD
(c) Main architectures: MLP, CNN and Autoencoders


 
References:





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  • Home
  • Courses
  • Timetable
  • Opportunities
  • Applying
  • Contact
  • Internship
  • Optimal transport
  • Algorithmic game theory
  • Neural network