UofU Data Science Courses

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

Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17

Delve into the concept of shattering and its application in analyzing infinite hypothesis spaces within machine learning theory and computational learning frameworks.

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

Agnostic Learning in Machine Learning - Lecture 16

Dive into computational learning theory and explore the fundamental concepts of agnostic learning in machine learning applications and theoretical frameworks.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Introduction to Dependency Parsing in Natural Language Processing

Master natural language processing techniques through dependency parsing, exploring syntactic relationships and grammatical structures in computational linguistics.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Constituency Parsing in Natural Language Processing

Master constituency parsing techniques and tree structures for analyzing syntactic relationships in natural language processing, focusing on hierarchical sentence decomposition.

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