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Informatique et Mathématiques appliquées
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> Formation > Cursus ingénieur

Pattern Recognition and Machine Learning - 5MM2537I

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  • Number of hours

    • Lectures : 18.0
    ECTS : 1.5

Goals

This course will provide an introduction to modern techniques for Bayesian pattern recognition and machine learning. We will begin with a review of linear regression, generative and discriminative methods for linear classification and the use of Kernel Methods and Support Vector Machines. We will then examine Bayesian Networks, Gaussian Mixture Models and principal components analysis.

The course will provide basic competence in machine learning methods that can be used to construct systems for data mining, communications, signal analysis, computer vision, speech recognition, man-machine interaction, and intelligent systems.

This course will be taught in French using an english language text book.

Contact James CROWLEY

Content

The course will closely follow the development of the text book by Chris Bishop's text book on Pattern Recognition and Machine Learning.

S1: Introduction, probability distributions, likelihood.
S2: Bayesian Probability Theory
S3: Gaussian Probability Density Functions
S4: Multivariate Gaussian Probability Density Functions
S5: Principal Component Analysis
S6: Mixture Models and Clustering
S7: Generative Methods for Classification
S8: Fisher Linear Discriminant and Perceptrons
S9: Perceptron and Kernel Methods
S10: Neural Networks
S11: Support Vector Machines
S12: Combining Models, Boosting.



Prerequisites

Linear Algebra,
Multivariate Calculus
Optimization
Probability and Statistics.

Tests

Semester 9 - The exam is given in english only 

CONTINUOUS ASSESSMENT :
Type of assessment (e.g.: practical work, regular attendance, participation):

ORDINARY SESSION:
Type of examination (written, oral, practical work): written examination
Specific room:
Duration: 3h
Allowed documents (e.g., none, summary on one A4 hand-written sheet, dictionary, every document): every document allowed
Unauthorized documents (e.g;, books, every document):
Material (e.g., calculators):

  • authorized material: calculators allowed
  • unauthorized documents: every other electronic material
    Comments:

RESIT EXAMINATION:
Type of examination (written, oral, practical work):
Specific room:
Duration: 3h
Allowed documents (e.g., none, summary on one A4 hand-written sheet, dictionary, every document): every document allowed
Unauthorized documents (e.g;, books, every document):
Material (e.g., calculators):

  • authorized material: calculators allowed
  • unauthorized documents: every other electronic material
    Comments:


N1 = 0.5E1 + 0.5TD
N2 = 0.5E2 + 0.5TD

Additional Information

Semester 9 - This course is given in english only EN

Curriculum->Math. Modelling, Image & Simulation->Semester 9

Bibliography

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer Verlag, 2006.
R.V. Hogg, J. M Kean and A.T. Craig.: Introduction to Mathematical Statistics (7th Edition). Pearson, United Kingdom (2012).

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Date of update January 15, 2017

Grenoble INP Institut d'ingénierie Univ. Grenoble Alpes