Probabilistic Machine Learning Courses and Certifications

UofU Data Science

Probabilistic Machine Learning - Lecture 15

Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical foundations and practical applications in modern data analysis and machine learning.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Machine Learning - Lecture 14

Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.

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

Probabilistic Graphical Models 3: Learning

305 ratings at Coursera

Learn how graphical models are learned from data: maximum likelihood and Bayesian parameter estimation, undirected model training, Bayesian network structure search, and EM for incomplete data.

• Coursera
• 8 weeks, 8 hours a week
• Self-Paced
• Paid Course

Career Certificate

TensorFlow 2 for Deep Learning

722 ratings at Coursera

Build, train and customise deep learning models in TensorFlow 2: regularisation, callbacks, lower-level APIs, custom layers, sequence models, and probabilistic models with TensorFlow Probability.

• Coursera
• 12 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

Computational and Graphical Models in Probability

Simulate random variables with the inverse transformation and rejection methods, then model relational data using exponential random graph models and probabilistic graphical models in R.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Unsupervised Machine Learning Hidden Markov Models in Python

4411 ratings at Udemy

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

• Udemy
• 9 hours 48 minutes
• Self-Paced
• Paid Course

UCLA Automated Reasoning Group

Automated Reasoning - Logic, Probabilistic Reasoning and Machine Learning

Explore Boolean logic, probabilistic reasoning, and machine learning through tractable circuits and knowledge compilation techniques for automated reasoning systems.

• YouTube
• 1 day 1 hour 51 minutes
• Self-Paced
• Free Video

Coursera

Probabilistic Graphical Models: A Compact Introduction

Build Bayesian and Markov network representations from conditional independence, run variable elimination and belief propagation, and weigh exact versus sampling inference in Python with pgmpy and pomegranate.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019)

Learn parameter estimation in Bayesian networks using maximum likelihood, Laplace smoothing, and expectation maximization.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Online Course

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Learn exact and approximate inference in hidden Markov models using forward-backward, particle filtering, and Gibbs sampling.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Online Course

Bayesian Networks 1 - Inference - Stanford CS221: AI

Learn how Bayesian networks compactly represent joint distributions and support probabilistic inference, including explaining away, evidence conditioning, and applications in tracking and classification.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Online Course

Validation of Safety Critical Systems

Master mathematical validation techniques for autonomous systems using sampling methods, formal verification, and reachability analysis in safety-critical applications.

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

VinAI

Probabilistic and Machine Learning Approaches for Autonomous Robots and Automated Driving

Explore probabilistic and machine learning approaches for autonomous robots and automated driving, focusing on robust perception and action execution in uncertain environments.

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

UofU Data Science

Probabilistic Machine Learning - Lecture 25

Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical and practical aspects of modern data analysis and machine learning techniques.

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

UofU Data Science

Probabilistic Machine Learning - Lecture 23

Dive into advanced data science concepts through comprehensive lecture materials from the University of Utah's graduate-level computer science program.

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