Pattern Recognition

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

Audience

• Data analysts
• PhD students, researchers and practitioners

Pattern Recognition is the process of classifying input data into objects or classes based on key features. 

This instructor-led, live training (online or onsite) 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.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

• Overview of pattern recognition and machine learning
• Key applications in various fields
• Importance of pattern recognition in modern technology

Probability Theory, Model Selection, Decision and Information Theory

• Basics of probability theory in pattern recognition
• Concepts of model selection and evaluation
• Decision theory and its applications
• Information theory fundamentals

Probability Distributions

• Overview of common probability distributions
• Role of distributions in modeling data
• Applications in pattern recognition

Linear Models for Regression and Classification

• Introduction to linear regression
• Understanding linear classification
• Applications and limitations of linear models

Neural Networks

• Basics of neural networks and deep learning
• Training neural networks for pattern recognition
• Practical examples and case studies

Kernel Methods

• Introduction to kernel methods in pattern recognition
• Support vector machines and other kernel-based models
• Applications in high-dimensional data

Sparse Kernel Machines

• Understanding sparse models in pattern recognition
• Techniques for model sparsity and regularization
• Practical applications in data analysis

Graphical Models

• Overview of graphical models in machine learning
• Bayesian networks and Markov random fields
• Inference and learning in graphical models

Mixture Models and EM

• Introduction to mixture models
• Expectation-Maximization (EM) algorithm
• Applications in clustering and density estimation

Approximate Inference

• Techniques for approximate inference in complex models
• Variational methods and Monte Carlo sampling
• Applications in large-scale data analysis

Sampling Methods

• Importance of sampling in probabilistic models
• Markov Chain Monte Carlo (MCMC) techniques
• Applications in pattern recognition

Continuous Latent Variables

• Understanding continuous latent variable models
• Applications in dimensionality reduction and data representation
• Practical examples and case studies

Sequential Data

• Introduction to modeling sequential data
• Hidden Markov models and related techniques
• Applications in time series analysis and speech recognition

Combining Models

• Techniques for combining multiple models
• Ensemble methods and boosting
• Applications in improving model accuracy

Summary and Next Steps