UofU Data Science Courses

Introduction to the Perceptron Algorithm - Lecture 8

Dive into the fundamental concepts of the Perceptron algorithm, exploring its core mechanisms and applications in neural network foundations.

• YouTube
• 18 minutes
• On-Demand
• Free Video

Online Learning and Mistake Bound Analysis - Lecture 6B

Explore online learning algorithms and performance quantification through mistake bound methodology, focusing on mistake-driven learning approaches.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Probabilistic Machine Learning - Lecture 23

Dive into advanced data science concepts through comprehensive lecture materials from the University of Utah's graduate-level computer science program.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Distance Metric Learning and Outlier Detection in Data Mining - Spring 2023

Delve into advanced data mining concepts, focusing on distance metric learning, outlier detection techniques, and practical applications of DBSCAN for effective data analysis.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Probabilistic Machine Learning - Lecture 22

Dive into advanced data science concepts and methodologies through comprehensive graduate-level instruction from the University of Utah's expert faculty.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Video

t-Distribution and Confidence Intervals - Lecture 14

Explore statistical inference through t-distributions, confidence intervals, and estimators, building essential skills for data analysis and probability interpretation.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Confidence Intervals and Margin of Error in Statistical Polling - Lecture 14B

Master confidence interval concepts through simulations, exploring margin of error in polling, and understanding how intervals reliably capture true population parameters.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Probabilistic Machine Learning - Lecture 21

Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Support Vector Machines and Regularized Risk Minimization - Lecture 21

Delve into advanced support vector machine concepts and regularized risk minimization principles for enhanced machine learning model development.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Support Vector Machines and Maximizing Margins in Machine Learning - Lecture 20

Dive into Support Vector Machines, exploring margin maximization, linear classifiers, and regularized risk minimization for advanced machine learning applications.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Multidimensional Scaling and Linear Discriminant Analysis in Data Mining - Spring 2023

Explore multidimensional scaling (MDS) and linear discriminant analysis (LDA), learning key concepts, motivations, and practical applications in data dimensionality reduction and classification.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Probability and Statistics: Estimation and Confidence Intervals for Normal Random Variables - Lecture 14

Master statistical estimation techniques and confidence intervals, focusing on normal random variables with known variance through practical examples and theoretical foundations.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Probabilistic Graphical Models and Mean Field Inference - Lecture 20

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.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Data Mining: Frequent Directions and Random Projections - Spring 2023

Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Boosting and Ensembles in Machine Learning - Lecture 19

Dive into advanced boosting and ensemble techniques in machine learning, exploring methods to combine multiple models for improved predictive performance and robustness.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video