UofU Data Science Courses

Language Modeling - Fundamentals and Applications

Master the fundamentals of language modeling, exploring statistical and neural approaches, probability distributions, and practical applications in natural language processing.

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

Neural Classification with Word Embeddings - PyTorch Tutorial

Master neural classification techniques and word embeddings implementation in PyTorch through hands-on practice with essential deep learning concepts and practical applications.

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

Vector Semantics and Word Embeddings in Data Science

Dive into the mathematical foundations of vector semantics and word embeddings, exploring how words are represented as numerical vectors for natural language processing applications.

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

Neural Networks Foundations - Feedforward Neural Networks

Master the fundamentals of feedforward neural networks, exploring their architecture, functionality, and implementation principles for building effective machine learning models.

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

Tokenization and Morphology in Natural Language Processing

Master essential text processing concepts through tokenization and morphology analysis, exploring fundamental techniques for breaking down and understanding linguistic structures.

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

Machine Learning Foundations - Logistic Regression

Master logistic regression fundamentals through hands-on practice, exploring core concepts, implementation techniques, and practical applications in machine learning and data analysis.

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

Reinforcement Learning from Human Feedback (RLHF) - Advances in Generative AI

Dive into reinforcement learning from human feedback (RLHF) and its crucial role in advancing generative AI systems, exploring key concepts, implementation strategies, and real-world applications.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Practical Advice for Building Machine Learning Applications - Lecture 28

Gain insights into practical strategies and best practices for implementing real-world machine learning systems, from system design to deployment considerations.

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

Topics in Responsible AI - Ethics, Fairness, and Accountability

Explore the critical principles and practices of responsible artificial intelligence, examining ethical considerations and implementation strategies for AI development.

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

Practical Issues with Neural Networks in Machine Learning - Lecture 27

Explore practical challenges and solutions for training large neural networks, including effective strategies and optimization techniques for improved performance.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

Neural Networks and Backpropagation - Lecture 26

Dive into neural network training fundamentals through backpropagation, learning essential parameter optimization techniques and mathematical foundations for deep learning implementation.

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

Neural Networks - Lecture 25

Dive into neural networks' structural foundations and discover their role as a hypothesis class, exploring key concepts and practical applications in machine learning.

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

Multilingual and Multimodal Large Language Models

Explore the fascinating world of multilingual and multimodal Large Language Models, understanding their capabilities, applications, and future potential in AI development.

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

Data Science: Introduction to Text Summarization

Master the art of condensing complex information into clear, concise summaries while learning key techniques and best practices for effective summarization.

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

Bayesian Learning and Maximum Likelihood Methods - Lecture 23

Explore probabilistic learning criteria through maximum a posteriori and maximum likelihood approaches, with practical examples of Bayesian learning applications.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video