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Introduction:

Automated Machine Learning (AutoML) is a rapidly growing field in artificial intelligence and data science. It aims to automate the process of model selection, hyperparameter tuning, and feature engineering, making machine learning more accessible to non-experts and saving valuable time and resources for experienced practitioners. In this thesis, we will focus on AutoML specifically for model selection, exploring the various techniques and algorithms available for automatically selecting the best model for a given dataset.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Research Problem
1.3 Research Objectives
1.4 Significance of the Study
1.5 Scope of the Study
1.6 Limitations of the Study

Chapter 2: Literature Review
2.1 Overview of Machine Learning and Model Selection
2.1.1 Traditional model selection methods
2.1.2 Challenges in manual model selection
2.2 Automated Machine Learning (AutoML) for Model Selection
2.2.1 Techniques and algorithms for AutoML
2.2.2 Comparison of AutoML tools

Chapter 3: Research Methodology
3.1 Data Preparation
3.2 Model Selection Techniques
3.3 Evaluation Metrics
3.4 Experimental Setup

Chapter 4: Discussion of Findings
4.1 Performance comparison of AutoML algorithms
4.2 Impact of dataset characteristics on model selection
4.3 Case studies and real-world applications of AutoML for model selection

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Directions for Research

Thesis Overview:

Automated Machine Learning for Model Selection is a comprehensive study that aims to explore the various techniques and algorithms available for automatically selecting the best model for a given dataset. The thesis will start with an introduction to the background and significance of AutoML for model selection, highlighting the research problem, objectives, scope, and limitations of the study.

The literature review will provide an overview of traditional model selection methods, the challenges in manual model selection, and the emerging field of AutoML for model selection. This chapter will also compare different AutoML tools and techniques in the context of model selection.

The research methodology chapter will outline the data preparation process, model selection techniques, evaluation metrics, and experimental setup for comparing the performance of various AutoML algorithms. The discussion of findings chapter will present the results of the experiments, including performance comparisons, the impact of dataset characteristics on model selection, and real-world applications of AutoML for model selection.

Finally, the conclusion and summary chapter will summarize the key findings of the study, highlight the contributions to the field, and suggest future directions for research in Automated Machine Learning for Model Selection.

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