Course Outline
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
Requirements
- Understanding of statistics
- Familiarity with multivariate calculus and basic linear algebra
- Some experience with probabilities
Audience
- Data analysts
- PhD students, researchers and practitioners
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
Contact us for an exact quote and to hear our latest promotions
Public Training
Please see our public courses
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
