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This information is indicative and can be subject to change. 
Topics in machine learning B

Teacher:  Sonia Vanier 

E-mail:  sonia.vanier@univ-paris1.fr
ECTS: 2.5
Evaluation:   Final exam
Previsional Place and time:  9 sessions (2h per session)

Prerequisites:  Attended both the first and second semester learning courses and familiar with programming.
Aim of the course:
 Syllabus: 

Optimization approaches for machine learning
 Nearest neighbor search:
• linear scan, kd-trees, k-nearest neighbors, application to retrieval in databases
Unsupervised learning 
•       k-means: objective function, Lloyd’s algorithm, initialization (random, k-means++), choosing k (silhouette)
•       hierarchical clustering  
•       density estimation(1h): parametric (Gaussians, mixtures), nonparametric (histograms , kernels), noise filtering, clustering (DBSCAN) 
Supervised learning 
•       Mathematical framework: loss function, risk minimization, regularization, Bayes’ classifier and consistency
•       k-NN classifier and regressor: universal consistency (Stone’s theorem), limitations
•       evaluation: confusion matrix, accuracy/precision/recall/F1, ROC/AUC, cross- validation
 
•       linear models for regression: quadratic loss and ordinary linear regression, basis functions, kernel trick
 
•       linear models for classification: logistic regression (logistic loss), Support Vector Machines (hinge loss), kernel trick again
→ show implementation using libSVM  
Feature extraction
•       A glimpse at feature design: descriptors for images/3d shapes/text/graphs
•       dimensionality reduction: curse of dimensionality, linear discriminant analysis (again), PCA
→ show implementation using eigen::SVD
Neural networks : Perceptron, MLP, back-propagation, a glimpse at various classes of networks (CNNs, RNNs, LSTMs, etc.)

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