UofU Data Science Courses

Linear Regression and Parameter Estimation in R - Lecture 17

Master linear regression techniques in R while exploring parameter estimation and random variable estimators through hands-on statistical analysis and practical applications.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

Probabilistic Machine Learning - Lecture 26

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 21 minutes
• Self-Paced
• Free Video

Confidence Intervals and Hypothesis Testing with R Examples - Lecture 16

Master statistical analysis through confidence intervals and hypothesis testing, with practical R programming examples for real-world data interpretation and decision-making.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

Introduction to Neural Networks - Lecture 25

Explore neural networks' fundamental concepts, structure, and prediction mechanisms while learning essential training approaches for building effective machine learning models.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Logistic Regression - Machine Learning Lecture 25

Dive into logistic regression classification, exploring maximum likelihood, posterior criteria, and loss minimization principles for effective machine learning model development.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Bayesian Learning: Maximum A Posteriori and Maximum Likelihood - Lecture 24

Delve into Bayesian learning principles, exploring maximum a posteriori and maximum likelihood learning criteria through practical examples and comprehensive demonstrations.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Probabilistic Machine Learning - Lecture 25

Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical and practical aspects of modern data analysis and machine learning techniques.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

Hypothesis Testing with t-Tests and p-Values - Spring 2023

Master statistical hypothesis testing through hands-on practice with t-tests and p-values, building essential skills for data-driven decision making and scientific research.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

PageRank Algorithm and Google's Web Search Formulation - Part 2

Dive into advanced PageRank concepts, exploring spider traps, dead ends, and Google's teleportation formulation while analyzing the algorithm's implementation and modern applications.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Hypothesis Testing: Three Steps and Examples with Height Analysis - Lecture 15B

Master hypothesis testing through a systematic 3-step approach, exploring real-world examples with height data and known variance to build practical statistical analysis skills.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Probabilistic Machine Learning - Lecture 24

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
• Self-Paced
• Free Video

Matrix Completion and PageRank in Data Mining - Spring 2023

Dive into matrix completion techniques and PageRank algorithms, exploring fundamental concepts for data mining and graph analysis applications.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

Bayesian Learning and Maximum Likelihood Methods - Lecture 23

Explore probabilistic learning criteria through maximum a posteriori and maximum likelihood examples, enhancing your understanding of Bayesian learning fundamentals.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Learning as Loss Minimization in Machine Learning - Lecture 23

Delve into the fundamental concept of learning as optimization, exploring how empirical risk minimization forms the foundation of machine learning algorithms.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Stochastic Gradient Descent for Support Vector Machines - Lecture 22

Dive into stochastic gradient descent algorithms and their application in optimizing Support Vector Machines through practical implementation techniques and mathematical foundations.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video