UofU Data Science Courses

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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video

Decision Tree Learning Algorithm and Practical Applications - Lecture 4

Dive into advanced decision tree learning algorithms and explore practical implementation challenges in machine learning applications.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Linear Models in Machine Learning - Lecture 5B

Explore fundamental concepts of linear classifiers and regressors, understanding their role in machine learning and various learning algorithms for hypothesis classification.

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

Understanding Overfitting in Machine Learning with Decision Trees - Lecture 5a

Understand how overfitting occurs in machine learning models, with practical examples using decision trees to identify and prevent this common challenge.

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

Data Mining: Understanding Min Hashing Techniques - Lecture 5

Dive into advanced min hashing techniques for efficient similarity computation and data processing, exploring algorithmic approaches for optimizing large-scale data operations.

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

Quantifying Learning Algorithms - Comparing Active Learning, Teaching, and Random Labeling - Lecture 6b

Explore how different learning protocols - active learning, teaching, and random labeling - impact concept acquisition and algorithm effectiveness in machine learning.

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

Linear Models Expressiveness - Lecture 6

Explore the expressive power and functional capabilities of linear models in machine learning, focusing on their versatility and practical applications.

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

Data Mining: Understanding Metric Distances - Lecture 7

Explore fundamental concepts of metric distances in data mining, including distance measurements, similarity metrics, and their applications in clustering and classification algorithms.

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

Decision Trees: Building and Learning with ID3 Heuristic - Lecture 3

Dive into the fundamentals of decision trees and master the ID3 heuristic algorithm, understanding key concepts and practical applications in machine learning.

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