UofU Data Science Courses

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

Taskification and Valid Benchmarks in Data Science

Explore the concept of taskification and its role in creating valid benchmarks for data science applications, focusing on practical implementation and evaluation methods.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Finetuning a Sequence-to-Sequence Model with RLHF

Master sequence-to-sequence model finetuning techniques and reinforcement learning from human feedback (RLHF) through hands-on demonstrations and practical implementations.

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

Finetuning DeBERTa for Instruction Fine-Tuning - Demo

Master advanced natural language processing techniques through hands-on instruction fine-tuning with DeBERTa, exploring implementation strategies and practical applications for enhanced model performance.

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

Linear Regression and Least Mean Square Method - Lecture 11

Master linear regression fundamentals through least mean square method, loss function minimization, and gradient descent techniques for practical machine learning applications.

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

Perceptron Mistake Bound Theorem in Machine Learning - Lecture 10

Dive into the mathematical foundations of Perceptron algorithms, exploring the theorem and proof behind mistake bounds in machine learning classification.

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

Introduction to the Perceptron Algorithm - Lecture 9

Dive into the fundamentals of Perceptron algorithm, exploring its intuitive principles, variations, and practical applications in neural network development.

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

Machine Learning: Representations and Learning in Online Systems - Lecture 9

Explore how hypothesis representation choices impact online learning algorithms and their effectiveness in machine learning applications.

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