Machine Learning Courses and Certifications

UofU Data Science

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

UofU Data Science

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

UofU Data Science

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

USC Information Sciences Institute

Designing Priors for Bayesian Neural Nets to Enhance Probabilistic Predictive Models in Engineering

Explore how Bayesian neural networks can enhance engineering predictive models by integrating physics-based knowledge and providing robust uncertainty quantification for high-consequence decision-making applications.

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

AutoML Seminars

Accurate Predictions on Small Data and Time Series with the Tabular Foundation Model TabPFN

Discover how TabPFN revolutionizes tabular data prediction with superior accuracy and speed, outperforming traditional methods across scientific fields while enabling fine-tuning and data generation capabilities.

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

NPTEL-NOC IITM

Evaluation of Unlearning and Graph Unlearning - Part 2

Explore evaluation methods for unlearning techniques including WMDP, TOFU, benchmark datasets, and graph unlearning applications.

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

NPTEL-NOC IITM

Naïve Bayes Classification Tutorial

Explore the application of Naïve Bayes Classification on a public dataset in this concise 16-minute tutorial from NPTEL-NOC IITM.

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

NPTEL-NOC IITM

K-Nearest Neighbor (k-NN) Classifier - Advantages, Limitations, and Examples

Explore the K-Nearest Neighbor (k-NN) Classifier, including its advantages, limitations, and practical applications through examples.

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

NPTEL-NOC IITM

Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM) Tutorial

Explore practical applications of PCA for dimensional reduction and GMM clustering on public datasets, including Silhouette Score calculation techniques.

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

Simons Institute

Weak-to-Strong Generalization Even in Random Feature Networks, Provably

Explore the phenomenon of Weak-to-Strong Generalization in random feature networks, where a student model outperforms its teacher despite being trained only on teacher-labeled data.

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

Simons Institute

A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI

Delve into the statistical foundations of contrastive pre-training and multimodal generative AI with Song Mei from UC Berkeley in this deep learning theory lecture.

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

Unify

Building an AI App - Part 6: Uploading Test Sets and Partitions

Dive into building an AI app for marking math questions, learning how to upload test sets and partitions to the Unify platform.

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

Cambridge Materials

Neural Functional Theory for Inhomogeneous (Non-)Equilibrium Fluids

Explore how machine learning enhances density functional theory for inhomogeneous fluids, enabling precise predictions and inverse design of nonequilibrium flow through neural networks trained with simulation data.

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

MonashPhysicsAndAstronomy

Time-Domain Science in the Rubin Era: Processing and Analyzing Astronomical Transients

Explore how Fink processes and analyzes astronomical transient events using machine learning, focusing on Rubin Observatory's capabilities for multi-wavelength astronomy and real-time data processing.

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

Labroots

Accessible Active Learning and LLMs to Enable Faster Iteration in Process Development and R&D

Discover how active learning frameworks and language models optimize lab automation for protein production, achieving 5x faster production rates and 2x higher protein yields through iterative processes.

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