UE Intro to Artificial Intelligence : Data Analysis and Mach

CONTENU

• Session 1

  General introduction (Rodrigo Cabral Farias)

  This lecture will introduce the main ingredients of ML, namely the different classes of problems, the data involved in such processes, the main classes of algorithms, and the learning process.

  Topics :

  • Data type
  • Supervised vs non-supervised learning
  • Algorithms taxonomy
  • Software platforms and languages
• Session 2

  Regression with the linear model (Rodrigo Cabral Farias)

  Regression is the problem concerned with the prediction a response value from variables. This course will cover the basics of the method including the selection of variables and the design of sparse models.

  Topics :

  • Linear regression and least squares
  • Errors and model adequacy
  • Sparse models
• Session 3

  Classification with the logistic regression (Rodrigo Cabral Farias)

  Logistic regression is a supervised classification algorithm used to model the probability of an observation to belong to a given class. To do so, a linear model is used to estimate the parameters.

  Topics :

  • Classification using linear models
  • The logistic regression
• Session 4

  Support Vector Machines (Rodrigo Cabral Farias)

  SVM are a popular and robust class of models to perform supervised classification. The main difficulties are to deal with classes which are partially mixed -- e.g. due to noise, and whose boundaries have a complex geometry.

  Topics :

  • Linear separability and support vectors
  • Soft margin separators
  • Kernels and non linear separation
  • Multiclass classification
• Session 5

  Linear Discriminant Analysis / Naive Bayes (Lionel Fillatre)

  LDA is another supervised classification algorithm using a linear combination of features defining boundaries separating two or more classes. This lecture will introduce LDA and compare it to the so-called Naive Bayes classifier.

  Topics :

  • Naive Bayes classifier
  • LDA
• Session 6

  CART / Decision Tree / Random Forest (Lionel Fillatre)

  Tree based models partition the data space to exploit local properties of the data, and can be used both for regression and classification. Multiple trees can also be combined to compensate the arbitrariness of the partitioning induced by a single tree.

  Topics :

  • Classification And Regression Trees
  • Decision tree based classification
  • Tree induction and split rules
  • Ensembles of decision trees and random forests
• Session 7

  Clustering (k-means, hclust) (Michel Riveil

  In a non supervised context, clustering aims at grouping the data in homogeneous groups by minimizing the intra-group variance.  This fundamental task is surprisingly challenging due to several difficulties: the (generally) unknown number of clusters, clusters whose boundaries have a complex geometry, dealing with overlapping clusters (due to noise), dealing with high dimensional data, etc. This class will present two main clustering techniques:

  Topics :

  •     k-means and k-means++
  • Hierarchical clustering
• Session 8

  Dimension reduction (PCA, t-SNE) (Michel Riveill)

  Dimensionality reduction methods aim at embedding high-dimensional data into a lower-dimensional space, while preserving specific properties such as pairwise distances, the data spread, etc. Originating with the celebrated Principal Components Analysis method, recent methods have focused on data located on non linear spaces.

  Topics :

  • Principal Component Analysis
  • t-Stochastic Neighbor Embedding (t-SNE)