Introduction to MACHINE and STATISTICAL LEARNING (30 hours, including courses and lab work)
Introduction to AI FOR AUTONOMOUS SYSTEMS (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
- 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
- Kmeans, Gaussian mixture model, EM algorithm,
- Ex MNIST data and comparison with CNN
- hierarchical patterns
AI for Autonomous systems
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.
AI prt (50%)
ML part (50%): 1h short written exam (25%), ML mini-project (25%)
The exam is given in english only
The course exists in the following branches:
Course ID : WEUMAIA0
You can find this course among all other courses.
Trevor Hastie, Robert Tibshirani et Jerome Friedman (2009), "The Elements of Statistical Learning," (2nd Edition) Springer Series in Statistics
Christopher M. Bishop (2006), "Pattern Recognition and Machine Learning," Springer
Kevin P. Murphy (2012), "Machine Learning: a Probabilistic Perspective", The MIT Press
Date of update September 3, 2020