UofU Data Science Courses

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

Natural Language Processing: POS Tagging, Named Entity Recognition, and Hidden Markov Models

Master natural language processing fundamentals through POS tagging, named entity recognition, and hidden Markov models for enhanced text analysis and linguistic structure prediction.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

Hidden Markov Models - Parameter Estimation and Efficient Inference

Master parameter estimation and efficient inference techniques for Hidden Markov Models, exploring key algorithms and mathematical foundations for probabilistic sequence modeling and pattern recognition.

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

Positive and Negative Learnability Results in Machine Learning - Lecture 14B

Explore fundamental learnability concepts in machine learning through Occam's razor theorem applications and function class analysis.

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

Machine Learning: Occam's Razor for Consistent Learners - Lecture 13B

Explore the theoretical foundations of Occam's razor theorem and its applications in consistent machine learning algorithms through mathematical analysis.

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

Introduction to Computational Learning Theory - Lecture 12

Explore fundamental concepts of computational learning theory and theoretical frameworks that underpin machine learning algorithms and their effectiveness.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

PAC Learning in Machine Learning Theory - Lecture 13

Explore the fundamental concepts of PAC (Probably Approximately Correct) learning theory and its applications in theoretical machine learning frameworks.

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

Machine Learning: Occam's Razor and Consistent Learners - Lecture 14

Delve into the principles of Occam's razor and its application in consistent learning algorithms, exploring key theoretical foundations of machine learning complexity.

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