Pattern Recognition Training Course

  1. Artificial Intelligence (AI) Training
  2. Machine Learning Training
  3. Pattern Recognition Training
  4. 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

This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.

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

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