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Introduction: Continual Learning is an important aspect of machine learning that allows models to adapt to evolving data over time. In a rapidly changing world where data is constantly being updated and new trends emerge, it is crucial for machine learning models to be able to learn from new data while retaining knowledge from previous experiences. This thesis aims to explore the concept of Continual Learning and its applications in adapting to evolving data.

Table of Contents:

Chapter One: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Objective of Study
1.4 Significance of Study
1.5 Limitation of Study
1.6 Scope of Study

Chapter Two: Literature Review
2.1 Overview of Continual Learning
2.2 Approaches to Continual Learning
2.3 Application of Continual Learning in Adapting to Evolving Data
2.4 Challenges in Continual Learning
2.5 Current Trends in Continual Learning

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Evaluation Metrics

Chapter Four: Discussion of Findings
4.1 Analysis of Results
4.2 Comparison of Different Continual Learning Approaches
4.3 Implications for the Field of Machine Learning
4.4 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Future Research
5.4 Contribution of the Study to the Field of Continual Learning

Thesis Overview:

Continual Learning is a critical aspect of machine learning that enables models to adapt to changing data over time. This thesis explores the concept of Continual Learning and its applications in adapting to evolving data. The study begins with an introduction that provides the background, problem statement, objective, significance, limitations, and scope of the research. The literature review covers the overview of Continual Learning, different approaches, its application in adapting to evolving data, challenges, and current trends in the field.

The research methodology section details the research design, data collection methods, data analysis techniques, and evaluation metrics used in the study. The discussion of findings chapter analyzes the results, compares different Continual Learning approaches, discusses implications for the field, and suggests future research directions. The conclusion and summary chapter summarizes the findings, presents the conclusion, recommends future research, and discusses the contribution of the study to the field of Continual Learning. This thesis aims to contribute to the growing body of knowledge on Continual Learning and its importance in adapting to evolving data in the field of machine learning.

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