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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
21 Hours
Testimonials (5)
Great contact with participants, practical knowledge which is highly valued. Adjustment of pace / speed. A huge plus, a mega-positive instructor, it's a shame that the training lasted only 2 days.
Marcin Mikielewicz - TECNOBIT SLU
Course - Introduction Deep Learning & Réseaux de neurones pour l’ingénieur
Machine Translated
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course - Applied AI from Scratch in Python
The instructors have extensive theoretical and practical knowledge. The instructors are communicative. During the course, participants could ask questions and receive satisfying answers.
Kamil Kurek - ING Bank Slaski S.A.; Kamil Kurek Programowanie
Course - Understanding Deep Neural Networks
Machine Translated
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Trainer explained complex and advanced topics very clearly.
Leszek K
Course - Artificial Intelligence Overview
Machine Translated
