UofU Data Science Courses

Interpretability of LLMs - Transcoders - Spring 2026

1 review

Explore transcoders in large language model interpretability through advanced computational techniques and analysis methods.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Utah CS 6966 Interpretability of LLMs - Course Review and Probing - Spring 2026

1 review

Explore interpretability techniques for large language models through comprehensive review and hands-on probing methods in this Utah CS graduate seminar.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Interpretability of LLMs - CLT to Attribution Graph - Spring 2026

1 review

Explore advanced techniques for understanding how large language models make decisions through attribution graphs and interpretability methods.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Data Mining: Randomness, Hash Functions and Probability Theory - Lecture 2

Explore fundamental concepts of data randomness, hash functions, and probability theory through birthday paradox and coupon collector problems in data mining.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Video

Introduction to Machine Learning: Fundamentals and Applications - Lecture 1

Discover the foundational concepts of machine learning, exploring its ubiquitous applications and understanding what constitutes learning in computational systems.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

Introduction to Data Mining - Lecture 1

Discover fundamental concepts and techniques of data mining, including pattern recognition, clustering, and predictive modeling for real-world applications.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Locality-Sensitive Hashing and Distribution Distances - Lecture 7

Discover LSH techniques for fast similarity search using hash functions and banding, plus explore distribution distances like KL, Hellinger, and Wasserstein metrics.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Jaccard Distance, K-grams, and MinHashing

Discover Jaccard Distance, k-grams modeling, and minHashing techniques for data similarity analysis and efficient approximate matching algorithms.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Neural Networks Foundations - Part 2

Dive into advanced neural network concepts and architectures to deepen your understanding of deep learning fundamentals and practical applications.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

Neural Networks Foundations - Part 1

Discover the fundamental concepts of neural networks in this comprehensive introduction covering basic architecture, key principles, and foundational theory.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Streaming Frequency Approximation - L13

Master streaming algorithms for frequency approximation including Misra-Gries, Count Sketch, and A-Priori methods in this data science lecture.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Data Mining: Locality-Sensitive Hashing for Similarity Measures - Lecture 6

Dive into Locality-Sensitive Hashing (LSH) techniques, exploring min-hash algorithms, Jaccard similarity, and triangle-based approaches for efficient data mining and similarity search.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Video

Mistake Bound Learning and Halving Bound Applications - Lecture 8a

Delve into mistake bound learning theory through practical examples and applications of the Halving bound in machine learning algorithms.

• YouTube
• 44 minutes
• On-Demand
• Free Video

The Perceptron Algorithm - Lecture 8b

Discover the fundamental concepts and implementation of the Perceptron algorithm, a foundational building block in machine learning and neural network development.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Online Learning and the Mistake Bound Model - Lecture 7

Explore the principles of online learning algorithms and performance quantification through the mistake bound model, focusing on theoretical foundations and practical applications.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video