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Dive into advanced data science concepts and methodologies through comprehensive graduate-level instruction from the University of Utah's expert faculty.
Explore statistical inference through t-distributions, confidence intervals, and estimators, building essential skills for data analysis and probability interpretation.
Master confidence interval concepts through simulations, exploring margin of error in polling, and understanding how intervals reliably capture true population parameters.
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Delve into advanced support vector machine concepts and regularized risk minimization principles for enhanced machine learning model development.
Dive into Support Vector Machines, exploring margin maximization, linear classifiers, and regularized risk minimization for advanced machine learning applications.
Explore multidimensional scaling (MDS) and linear discriminant analysis (LDA), learning key concepts, motivations, and practical applications in data dimensionality reduction and classification.
Master statistical estimation techniques and confidence intervals, focusing on normal random variables with known variance through practical examples and theoretical foundations.
Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.
Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.
Dive into advanced boosting and ensemble techniques in machine learning, exploring methods to combine multiple models for improved predictive performance and robustness.
Delve into advanced computational learning theory concepts and VC dimensions, exploring fundamental principles that shape machine learning algorithm capabilities and limitations.
Master fundamental statistical concepts including Central Limit Theorem, estimation methods, bias analysis, and statistical efficiency principles for robust data analysis and interpretation.
Explore random projection techniques in data mining, from PCA and SVD fundamentals to Johnson-Lindenstrauss lemma, with practical algorithms and theoretical foundations.
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