Executive Development Programme in Likelihood-Based Methods for Machine Learning

This program is designed for seasoned data scientists, managers, and executives seeking to enhance their understanding of likelihood-based methods in machine learning. Participants will gain a deep grasp of statistical models, including maximum likelihood estimation, Bayesian methods, and likelihood ratio tests, essential for making informed decisions in data-driven projects.

Upon completion, attendees will be able to apply these methods to real-world problems, improve model accuracy, and lead teams in developing robust machine learning solutions. The program includes hands-on workshops and case studies to bridge theory with practical application.

1. 1. Introduction to Likelihood-Based Methods: Learners will understand the fundamental concepts of likelihood functions, maximum likelihood estimation, and their role in machine learning models. They will gain skills in formulating likelihood functions and estimating parameters for simple models.
2. 2. Likelihood-Based Classification Methods: This module covers likelihood-based approaches for classification tasks, including logistic regression and probabilistic classifiers. Learners will learn to implement and evaluate these methods on real-world datasets.
3. 3. Likelihood in Regression Analysis: Focusing on regression models, learners will explore the use of likelihood functions to fit linear and generalized linear models. Practical skills in model fitting, diagnostics, and prediction will be developed.
4. 4. Advanced Likelihood-Based Techniques: Advanced topics such as expectation-maximization algorithms and profile likelihoods are covered. Learners will learn to apply these techniques to complex data structures and model fitting scenarios.
5. 5. Bayesian Likelihood Methods: This module introduces Bayesian approaches to likelihood-based methods, covering prior distributions, posterior inference, and Markov Chain Monte Carlo (MCMC) techniques. Practical skills in implementing Bayesian models will be developed.
6. 6. Likelihood-Based Model Selection: Learners will study methods for model selection using likelihood criteria such as AIC and BIC. Practical exercises will include model comparison and selection based on these criteria.
7. 7. Likelihood in Neural Networks: This module explores the use of likelihood in deep learning, particularly in the context of neural networks. Learners will understand how likelihood-based methods can be applied to improve the training and validation of neural networks.
8. 8. Likelihood-Based Methods in Time Series Analysis: Focusing on time series data, learners will learn to apply likelihood methods to model temporal dependencies and forecast future values. Practical skills in time series analysis will be developed.
9. 9. Likelihood in Computer Vision: This module covers the application of likelihood-based methods in computer vision tasks such as image classification and object detection. Practical skills in implementing and evaluating these methods will be developed.
10. 10. Case Studies in Likelihood-Based Methods: In this final module, learners will apply likelihood-based methods to real-world case studies from various domains. This will provide an opportunity to integrate and apply the knowledge and skills learned throughout the programme.