UofU Data Science Courses

Neural Networks: Practical Aspects of Training and Implementation - Lecture 26

Dive into practical aspects of using and training neural networks, focusing on implementation techniques and methodologies.

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

Neural Networks: Forward Propagation and Backpropagation - Lecture 25

Dive into neural network training with forward propagation and backpropagation techniques for parameter optimization in this comprehensive exploration.

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

Neural Networks - Lecture 24b

Explore neural networks from a structural perspective, understanding their hypothesis class and forward propagation for making predictions.

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

Loss Minimization Revisited - Lecture 24a

Dive into loss minimization concepts, exploring perceptron, hinge, and logistic losses for machine learning applications.

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

Graph Embeddings - Lecture 25

Explore graph embeddings from MDS and Laplacian-based approaches to DeepWalk, with insights on word vector embeddings and bias in this comprehensive lecture.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

Practical Advice for Using Machine Learning - Lecture 27

Gain insights into practical advice for building machine learning systems in this comprehensive lecture that concludes the course.

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

Data Mining Lecture 19 - Distance Metric Learning

Explore distance metric learning concepts and techniques in this comprehensive data mining lecture from UofU Data Science.

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