Course

Evolutionary Machine Learning Basics: Fundamentals of Evolutionary Machine Learning – Introduction to Evolutionary Computation, Biological Inspiration behind Evolutionary Algorithms. Evolutionary Supervised Machine Learning – Introduction to Supervised Learning, Evolutionary Algorithms for Regression and Classification, Feature Selection using Evolutionary Algorithms. Evolutionary Machine Learning for Unsupervised Learning – Introduction to Unsupervised Learning, Evolutionary Clustering Algorithms, Evolutionary Dimensionality Reduction Techniques. Evolutionary Computation and the Reinforcement Learning Problem – Introduction to Reinforcement Learning, Evolutionary Algorithms for Policy Optimization, Balancing Exploration and Exploitation using Evolutionary Techniques.

Evolutionary Computation as Machine Learning: Evolutionary Regression and Modelling – Evolutionary Algorithms for Regression Problems, Model Selection and Optimization using Evolutionary Techniques. Evolutionary Clustering and Community Detection – Community Detection in Networks using Evolutionary Techniques. Evolutionary Classification – Evolutionary Algorithms for Binary and Multi-class Classification, Hyperparameter Tuning and Model Selection using Evolutionary Techniques. Evolutionary Ensemble Learning – Evolutionary Algorithms for Ensemble Model Construction, Diversity and Performance Optimization using Evolutionary Techniques.

Evolutionary Computation for Machine Learning: Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT), Evolutionary Model Validation—An Adversarial Robustness Perspective, Evolutionary Approaches to Explainable Machine Learning, Evolutionary Algorithms for Fair Machine Learning.

Preamble

This course explores the intersection of evolutionary computation and machine learning. It provides an in-depth understanding of how evolutionary techniques can enhance machine learning methods. The course covers a wide range of topics, from fundamental concepts to advanced applications, and equips students with the skills to apply these techniques to real-world problems. It’s a blend of theoretical knowledge and practical implementation, designed to prepare students for future academic or professional roles in this exciting field.

Course Objectives

• To understand the fundamental concepts of evolutionary algorithms and their applications in various learning paradigms in machine learning.
• To develop proficiency in implementing evolutionary computation techniques for clustering, classification, regression, and ensemble learning.
• To learn to enhance machine learning models through evolutionary approaches for data preparation, model parametrization, design, and validation.
• To critically evaluate and compare the effectiveness of evolutionary computation methods with traditional machine learning techniques.

Course Outcomes

Prerequisites

• Mathematical Foundations of Computing
• Machine Learning

CO-PO Mapping

Evaluation Pattern – 70:30

• Midterm Exam – 20%
• Lab Assignments – 50%
• End Semester Exam – 30%

Text Book / References

1. Banzhaf, W., Machado, P., Zhang, M. (Eds.) (2023). Handbook of Evolutionary Machine Learning. Springer.
2. Song, T., Zheng, P., Wong, M. L. D., Wang, X. (2019). Bio-Inspired Computing Models and Algorithms. World Scientific.
3. Floreano, D., Mattiussi, C. (2008). Bio-Inspired Artificial Intelligence. MIT Press.
4. Mitchell, T. (1997). Machine Learning. McGraw Hill.
5. Eiben, A. E., Smith, J. E. (2015). Introduction to Evolutionary Computing. Springer.