MGT-448 / 4 crédits

Enseignant: Kiyavash Negar

Langue: Anglais

## Summary

This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining.

## Content

List of topics:

• General Introduction
• Supervised Learning, Discriminative Algorithms:
Supervised Learning Concept, Linear Regression, Maximum Likelihood, Normal Equation Gradient Descent, Stochastic Gradient, SVRG.
Linear Classification, Logistic Regression, Newton Method,
• Generative Algorithms:
Multivariate Normal, Linear Discriminant Analysis
Naive Bayes, Laplacian Smoothing
Multiclass Classification, K-NN
Multi-class Fisher Discriminant Analysis, Multinomial Regression
Support Vector Machines and Kernel Methods:
Intuition, Geometric Margins, Optimal Margin Classifier
Lagrangian Duality, Soft-margin, Loss function, Stochastic Subgradient Method. Kernel, SMO algorithm, Coordinate Gradient Descent.
Kernel PCA, Kernel Logistic Regression, Kernel Ridge Regression, Multiclass SVM
• Unsupervised Learning:
PCA, Mixture Models, Bayesian Graphical Models
Power Method, Ojaâ€™s algorithm, EM Algorithm, Variational Inference Matrix Factorization/Completion
• Regularization and Model Selection:
Cross Validation, Hill Climbing, Bayesian Optimization Bayesian Regression, Bayesian Logistic Regression
Forward and Backward Regression, Lasso, elastic-net. Proximal Gradient, Prox-SVRG.
Coordinate Proximal Gradient, Pathwise Coordinate Descent
• Decision Tree and Random Forest:
Entropy, Building Tree
Bagging features, Bagging Samples, Random Forest Adaboost, Gradient Tree Boosting
• Neural Network:
Concept; Deep Neural Network; Backpropagation Convolutional Neural Network;

## Keywords

Supervised and unsupervised learning, Model selection, Generative models.

## Required courses

A course in basic probability theory.

## Recommended courses

linear algebra and statistics.

## Important concepts to start the course

Students should be familiar with basic concepts of probability theory, calculus and linear algebra.

## Learning Outcomes

By the end of the course, the student must be able to:

• Formalize Formulate supervised and unsupervised learning problems and apply it to data.
• Understand and apply generative models.
• Understand and train basic neural networks and apply them to data.

## Transversal skills

• Assess one's own level of skill acquisition, and plan their on-going learning goals.

## Teaching methods

Classical formal teaching interlaced with practical exercices.

## Expected student activities

Active participation in exercise sessions is essential.

## Assessment methods

30% Homework

20% Midterm project

50% Final project

## Supervision

 Office hours Yes Assistants Yes Forum No

## Dans les plans d'études

• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Statistical inference and machine learning
• Cours: 2 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel

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