Pattern Recognition Training Course

Course Code

datamodeling

Duration

21 hours (usually 3 days including breaks)

Requirements

  • Understanding of statistics.
  • Familiarity with multivariate calculus and basic linear algebra.
  • Some experience with probabilities.

Audience

  • Data analysts
  • PhD students, researchers and practitioners

Overview

Αυτή η μάθημα καθοδηγείται από εκπαιδευτές παρέχει μια εισαγωγή στο πεδίο της αναγνώρισης προτύπων και της μηχανικής μάθησης. Ασχολείται με πρακτικές εφαρμογές στις στατιστικές, την επιστήμη των υπολογιστών, την επεξεργασία σήματος, την ηλεκτρονική όραση, την εξόρυξη δεδομένων και τη βιοπληροφορική.

Το μάθημα είναι διαδραστικό και περιλαμβάνει πολλές πρακτικές ασκήσεις, ανατροφοδότηση εκπαιδευτών και δοκιμή γνώσεων και δεξιοτήτων που αποκτήθηκαν.

Machine Translated

Course Outline

Introduction

Probability Theory, Model Selection, Decision and Information Theory

Probability Distributions

Linear Models for Regression and Classification

Neural Networks

Kernel Methods

Sparse Kernel Machines

Graphical Models

Mixture Models and EM

Approximate Inference

Sampling Methods

Continuous Latent Variables

Sequential Data

Combining Models

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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