UofU Data Science Courses

Attention Mechanisms in Deep Learning

Explore attention mechanisms in deep learning and neural networks through comprehensive theoretical foundations and practical implementations.

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

Sequence-to-Sequence Models Through the Lens of Neural Machine Translation

Explore sequence-to-sequence models and neural machine translation fundamentals in this comprehensive data science presentation from University of Utah.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Subword Tokenization

Discover how to break down words into smaller meaningful units for improved natural language processing and machine learning applications.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Streaming Distinct Item Counting and Mergeable Summaries - HyperLogLog and Bloom Filters - L14

Discover advanced streaming algorithms including HyperLogLog for distinct item counting, Bloom filters, and mergeable data summaries for large-scale data processing.

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

Functions and Gradients - Cross-Validation, Double Descent, and Gradient Descent Preparation - L14

Explore cross-validation, double descent phenomenon, and gradient descent fundamentals including functions, convexity, derivatives, and gradients for data analysis.

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

Cross-Validation in Data Analysis - Lecture 13

Master cross-validation techniques to evaluate model performance and select optimal parameters like polynomial degree using train/test and train/validation/test splits.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Welcome to Data Mining - L1

Discover data mining fundamentals through course mechanics and comprehensive overview in this introductory session from UofU Data Science.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Course Overview and Logistics

Discover the structure and expectations for your data science journey through essential course logistics and overview.

• YouTube
• 1 hour 27 minutes
• Self-Paced
• Free Video

Machine Learning Foundations - Part 1

Discover fundamental machine learning concepts and principles in this comprehensive introduction covering essential theory and practical applications.

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

Statistical Principles for Data Mining - L2

Discover probabilistic modeling fundamentals for data mining including IID samples, hashing techniques, birthday paradox, and coupon collector problems.

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

Parameter-Efficient Finetuning - QLoRA and LoRA Methods

Explore parameter-efficient fine-tuning techniques with LoRA and QLoRA, learning how to optimize large language models while maintaining performance and reducing computational costs.

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

Data Mining: Hierarchical and Density-Based Clustering Methods - Lecture 11

Explore hierarchical clustering techniques including HAC, DBScan, and density-based methods while mastering key concepts in data grouping and cluster analysis.

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

Data Mining: Assignment-Based Clustering and k-Means Algorithms - Lecture 12

Dive into clustering algorithms like k-means, k-center, and k-median, exploring implementation techniques through Gonzalez, Lloyd's, and k-means++ algorithms for effective data grouping.

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

Perceptron Algorithm and Mistake Bound Theorem - Lecture 9

Dive into advanced Perceptron concepts, exploring practical algorithm variants and the mistake bound theorem for enhanced machine learning understanding.

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

Distances for Distributions in Data Mining - Lecture 10

Explore statistical methods for measuring distances between probability distributions, including key concepts and practical applications in data mining and analysis.

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