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Statistical machine learning focuses on developing algorithms and models that can automatically learn from data and make predictions or decisions without being explicitly programmed. Key aspects of statistical machine learning include Supervised Learning (such as linear regression, logistic regression, decision trees, support vector machines (SVM) and neural networks), unsupervised Learning and Dimensionality reduction techniques, Semi-Supervised Learning and Reinforcement Learning. Reinforcement learning is commonly used in areas such as robotics, game playing and autonomous systems.
Statistical machine learning and computational statistics are closely intertwined, with computational techniques enabling the application of statistical models to large datasets and complex problems. These fields play a fundamental role in various domains, including image and speech recognition, natural language processing, recommendation systems, bioinformatics, finance and many others.

Research faculty members in this area