Artificial Intelligence and Autonomous Systems (M2-MARS / ASI) - WEUMAIA0

Goal(s)

The purpose of this course is to give an overview of the main techniques of artificial intelligence and a thorough understanding of how to apply them to the robotics and autonomous systems.

Content(s)

*Introduction to AI FOR AUTONOMOUS SYSTEMS (30 hours, including courses and lab work)

*Introduction to MACHINE and STATISTICAL LEARNING (30 hours, including courses and lab work)

MACHINE LEARNING and Statistical LEARNING PRINCIPLES (ML)
This part of the course aims at providing the students with the basic methods and statistical/mathematical tools to understand the most classical approaches used in both supervised or unsupervised learning, as well as understanding their capabilities, characteristics and limitations.

ML-1: "Introduction to Machine Learning
- motivations
- basic challenges: classification/regression, Supervised/unsupervised, cost functions, training error/testing
- examples of approaches WITH and WITHOUT models
- Notion of complexity: variance bias trade-off (over or under learning), scourge of the dimension

ML-2: Generative Approaches :
- Discriminant analysis (linear/quadratic, regularization, size reduction)
- Naive Bayesian (parametric and non-parametric
- examples given MNIST, ...

ML-3: Discriminatory approaches :
- generalized linear statistical models for regression and classification (logistic regression)
- ridge regulation, lasso (parsimony bet)
- Complexity and validation (cross-validation) of models
- Data transformation (kernel move)
- Examples given MNIST, ...

ML-4: "DATA REPRESENTATION" (Unsupervised).
- BCP, standardization, small visualization

ML-5. "(Convolutional) Neural Nets."
- Notion of neuronal learning, gradient stoch. GRADIENT, NN
- Deep, convolutional aspects... motivations and examples (scikit-learn, kheras/tensorflow,...)

ML-6. Random trees and forests
- decision tree, random forests

ML-7. "CLUSTERING
- Kmeans, Gaussian mixture model, EM algorithm,
- Ex MNIST data and comparison with CNN
- hierarchical patterns

AI for Autonomous systems

ML :
probabilities and statistics, main distributions, probability density functions, matrix calculation, basic optimization (Newton's methods, Gradient methods...)
The math reminders will be done in general (the prerequisites having been seen in 1A and 2A), but very quickly.