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Non-convex optimization for local minima – Complete Phd and Masters Thesis

[ad_1] Introduction Non-convex optimization is a challenging field in mathematics and computer science that deals with finding the optimal solutions for problems that do not have convex objective functions. Local minima are a common issue…

Convex optimization for global solutions – Complete Phd and Masters Thesis

[ad_1] Introduction Convex optimization is a powerful mathematical tool that has been widely used in various fields such as machine learning, signal processing, control systems, and operations research. It involves the optimization of convex objective…

Submodular optimization for diversity and coverage – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, there has been a growing interest in submodular optimization for diversity and coverage in various fields such as machine learning, data mining, and artificial intelligence. Submodular functions have the property…

Collaborative filtering for recommendation – Complete Phd and Masters Thesis

[ad_1] Introduction Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on their preferences and behaviors. With the increasing amount of information available online, the need for…

Tensor factorization for multi-way analysis – Complete Phd and Masters Thesis

[ad_1] Introduction Tensor factorization is a powerful technique used in multi-way data analysis to decompose high-dimensional tensors into a set of lower-dimensional factors. It has gained popularity in various fields such as signal processing, image…

Non-negative matrix factorization for parts-based decomposition – Complete Phd and Masters Thesis

[ad_1] Introduction Non-negative matrix factorization (NMF) is a powerful tool in data analysis and signal processing that aims to extract meaningful and interpretable parts-based representation of data. It has gained popularity in various fields such…

Sparse coding for efficient representation – Complete Phd and Masters Thesis

[ad_1] Introduction Sparse coding is a powerful technique in the field of machine learning and signal processing that aims to efficiently represent data using a small number of non-zero coefficients. It has been widely used…

Metric learning for similarity measurement – Complete Phd and Masters Thesis

[ad_1] Introduction: In the field of machine learning and pattern recognition, the measurement of similarity between data points is a crucial task with implications in various applications such as image retrieval, recommendation systems, and text…

Ensemble learning for combining models – Complete Phd and Masters Thesis

[ad_1] Introduction Ensemble learning is a machine learning approach that aims to combine multiple models to improve the overall performance of a predictive task. By leveraging the diversity of multiple models, ensemble learning can often…

Unsupervised learning for discovering patterns – Complete Phd and Masters Thesis

[ad_1] Introduction Unsupervised learning is an important branch of machine learning that focuses on discovering patterns in data without the need for labeled examples. Unsupervised learning algorithms aim to find hidden structures or relationships in…

Tag: 5 year master’s program computer science