UofU Data Science Courses

Neural Networks - Lecture 25

Dive into neural networks' structural foundations and discover their role as a hypothesis class, exploring key concepts and practical applications in machine learning.

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

Multilingual and Multimodal Large Language Models

Explore the fascinating world of multilingual and multimodal Large Language Models, understanding their capabilities, applications, and future potential in AI development.

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

Data Science: Introduction to Text Summarization

Master the art of condensing complex information into clear, concise summaries while learning key techniques and best practices for effective summarization.

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

Bayesian Learning and Maximum Likelihood Methods - Lecture 23

Explore probabilistic learning criteria through maximum a posteriori and maximum likelihood approaches, with practical examples of Bayesian learning applications.

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

Logistic Regression - Machine Learning Lecture 24

Master logistic regression classification through maximum likelihood and maximum a posteriori criteria, building practical skills for effective machine learning model development.

• YouTube
• 43 minutes
• Self-Paced
• 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 16 minutes
• Self-Paced
• Free Video

Stochastic Gradient Descent for Support Vector Machines - Lecture 22A

Dive into stochastic sub-gradient descent optimization for Support Vector Machines and discover its connection to perceptron algorithms through practical implementation techniques.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Maximum Likelihood Estimation for Regression in Machine Learning - Lecture 24

Dive into maximum likelihood estimation for regression through practical examples and explore its connection to loss minimization in Bayesian learning frameworks.

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

Support Vector Machines: Theory and Applications - Lecture 21

Dive into advanced concepts of Support Vector Machines (SVMs), exploring their mathematical foundations and practical applications in machine learning classification problems.

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

Stochastic Gradient Descent for Support Vector Machines - Lecture 21B

Dive into optimization techniques for Support Vector Machines, focusing on stochastic gradient descent methods and their practical implementation in machine learning algorithms.

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

Coreference Resolution in Natural Language Processing

Master the fundamentals of coreference resolution in natural language processing, exploring key concepts, algorithms, and practical applications for improving text understanding and analysis.

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

Semantic Parsing and Semantic Role Labeling

Master semantic parsing and Semantic Role Labeling (SRL) techniques to understand natural language structure, relationships between predicates and arguments, and core principles of computational linguistics.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Boosting and Ensemble Methods in Machine Learning - Lecture 18

Dive into boosting algorithms and learn how to combine weak learning rules to create powerful predictive models in machine learning applications.

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

Machine Learning: Boosting and Ensemble Methods - Lecture 19

Dive into advanced boosting techniques and ensemble methods to transform weak learning algorithms into powerful predictive models for enhanced machine learning capabilities.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

The VC Dimension in Machine Learning - Lecture 18

Delve into the fundamental concept of VC dimension in machine learning theory, exploring its relationship with shattering and its implications for learning algorithms.

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