UofU Data Science Courses

Logistic Regression - Lecture 23b

Dive into logistic regression classification, learning maximum likelihood and maximum a posteriori criteria, and understanding how it fits within loss minimization frameworks.

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

Bayesian Learning: Connection to Least Mean Square Regression - Lecture 23a

Delve into Bayesian learning concepts and their connection to least mean square regression in this continuation of the series.

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

Data Mining Lecture 24 - Graph Communities

Explore graph communities in social networks, including Erdos-Renyi random graphs, preferential attachments, betweenness, and modularity concepts.

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

Data Mining Lecture 20 - Outliers

Explore outlier detection methods and techniques in data mining, understanding how to identify and handle anomalous data points.

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

Support Vector Machines: Maximizing Margins and Linear Classifiers - Lecture 19

Dive into support vector machines, exploring margin maximization, linear classifiers, SVM objectives, and regularized risk minimization concepts in machine learning.

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

Practical Machine Learning Tutorial with Scikit-Learn - Lecture 20

Gain insights into setting up a machine learning workflow with scikit-learn through this hands-on tutorial.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Boosting and Ensembles - Lecture 18a

Dive into advanced boosting and ensemble methods for machine learning, continuing the exploration of these powerful techniques for improved model performance.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

Boosting: Creating Strong Learners from Weak Learners - Lecture 17

Dive into AdaBoost algorithm and the concept of boosting to transform weak learners into strong ones in machine learning applications.

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

VC Dimensions (Continued) - Lecture 16

Dive into computational learning theory and VC dimensions in this concluding lecture on the topic.

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

Support Vector Machines - Lecture 18b

Explore the concept of maximizing margins in support vector machines to enhance model generalizability in this concise machine learning lecture.

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

CS 3130/ECE 3530 - Lecture 19

Dive into key concepts and applications of computer science and electrical engineering in this comprehensive university lecture from the University of Utah's Data Science program.

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

Data Mining Lecture 18 - Matrix Sketching

Explore matrix sketching techniques in data mining, understanding how to efficiently represent large matrices with smaller ones while preserving essential properties.

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

Faster and Cheaper LLMs with Weight and Key-value Cache Quantization

Explore advanced techniques for optimizing Large Language Models through weight and key-value cache quantization methods to improve speed and reduce computational costs.

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