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What are some viable machine learning research topics for a bachelor’s thesis that can be explored using average computers?
Supervised Learning with Decision Trees: Explore the implementation and performance of decision tree algorithms on small to medium-sized datasets, comparing their accuracy and interpretability to other classical supervised learning methods.
Ensemble Methods for Classification: Investigate the use of ensemble techniques, such as random forests and boosting, to improve the predictive power of machine learning models on real-world classification problems.
Unsupervised Clustering Algorithms: Analyze the effectiveness of k-means and hierarchical clustering algorithms in grouping data points based on their inherent similarities, using datasets that can be handled by average computers.
Dimensionality Reduction with Principal Component Analysis (PCA): Implement PCA to study the impact of feature reduction on the performance of machine learning models, particularly in domains with high-dimensional data.
Machine Learning in Social Science Research: Explore the application of traditional statistical methods, such as linear/logistic regression and time series analysis, to predict social phenomena or trends using publicly available datasets.
Healthcare Analytics with Machine Learning: Investigate the use of machine learning techniques, such as classification and regression, to analyze healthcare data (e.g., disease prediction, patient outcome forecasting) on average computing resources.
Environmental Data Analysis using Machine Learning: Leverage machine learning models to identify patterns, trends, or anomalies in environmental datasets (e.g., climate, weather, pollution) that can be processed on standard computers.
Interpretability and Fairness in Machine Learning: Develop methods to improve the interpretability of machine learning models and explore techniques to assess and mitigate potential biases in the decision-making process.
Transfer Learning for Computer Vision Tasks: Explore the feasibility of using pre-trained models (e.g., from popular computer vision tasks) and fine-tuning them on smaller datasets that can be handled by average computers.
Anomaly Detection in Time Series Data: Investigate the use of unsupervised learning approaches, such as one-class support vector machines or isolation forests, to identify anomalies in time series data that can be processed on standard hardware.
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