Course Outline
Introduction
- Overview of pattern recognition and machine learning for government applications
- Key applications across various fields, including public sector workflows
- Importance of pattern recognition in modern technology for government operations
Probability Theory, Model Selection, Decision and Information Theory
- Basics of probability theory in pattern recognition for government use cases
- Concepts of model selection and evaluation in public sector contexts
- Decision theory and its applications in governmental decision-making processes
- Information theory fundamentals relevant to data governance and accountability
Probability Distributions
- Overview of common probability distributions used for government data analysis
- Role of distributions in modeling public sector data
- Applications in pattern recognition for government services
Linear Models for Regression and Classification
- Introduction to linear regression for government datasets
- Understanding linear classification in the context of public sector applications
- Applications and limitations of linear models for government use
Neural Networks
- Basics of neural networks and deep learning for government applications
- Training neural networks for pattern recognition in governmental data
- Practical examples and case studies relevant to public sector workflows
Kernel Methods
- Introduction to kernel methods in pattern recognition for government use
- Support vector machines and other kernel-based models for governmental data analysis
- Applications in high-dimensional data for government datasets
Sparse Kernel Machines
- Understanding sparse models in pattern recognition for government applications
- Techniques for model sparsity and regularization in public sector contexts
- Practical applications in data analysis for government operations
Graphical Models
- Overview of graphical models in machine learning for government use
- Bayesian networks and Markov random fields for governmental data modeling
- Inference and learning in graphical models for public sector workflows
Mixture Models and EM
- Introduction to mixture models for government applications
- Expectation-Maximization (EM) algorithm for governmental data analysis
- Applications in clustering and density estimation for public sector datasets
Approximate Inference
- Techniques for approximate inference in complex models for government use
- Variational methods and Monte Carlo sampling for large-scale governmental data analysis
- Applications in analyzing extensive public sector datasets
Sampling Methods
- Importance of sampling in probabilistic models for government applications
- Markov Chain Monte Carlo (MCMC) techniques for governmental data analysis
- Applications in pattern recognition for public sector datasets
Continuous Latent Variables
- Understanding continuous latent variable models for government use
- Applications in dimensionality reduction and data representation for public sector data
- Practical examples and case studies relevant to governmental workflows
Sequential Data
- Introduction to modeling sequential data for government applications
- Hidden Markov models and related techniques for governmental time series analysis
- Applications in time series analysis and speech recognition for public sector use cases
Combining Models
- Techniques for combining multiple models for government applications
- Ensemble methods and boosting for improving model accuracy in public sector workflows
- Applications in enhancing the reliability of governmental data analysis
Summary and Next Steps
Requirements
- Understanding of statistics for government applications
- Familiarity with multivariate calculus and basic linear algebra
- Some experience with probabilities
Audience
- Data analysts for government agencies
- PhD students, researchers, and practitioners in public sector roles
Testimonials (5)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
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The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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Very flexible.
Frank Ueltzhoffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
