Monte Carlo Methods Courses and Certifications

Dave Churchill

Intro to Artificial Intelligence - RL Monte Carlo Methods - Lecture 18

Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Dave Churchill

Intro to Artificial Intelligence - RL Monte Carlo Methods - Lecture 17

Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.

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

Dave Churchill

Introduction to Artificial Intelligence: Monte Carlo Reinforcement Learning Methods - Lecture 16

Explore Monte Carlo reinforcement learning methods, including prediction and policy iteration, with applications to Blackjack and matchbox machine learning.

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

Prof. Ryan C. Cooper

Monte Carlo Pi Methods: Technical Editing - Lecture ME3256

Enhance technical writing skills for Monte Carlo pi methods. Learn effective editing techniques to improve clarity and precision in scientific reports.

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

Dave Churchill

Introduction to Artificial Intelligence - Lecture 18 - Monte Carlo Methods

Explore Monte Carlo methods in artificial intelligence, learning algorithmic techniques for modern problem-solving and game applications.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Probability in Practice

Apply probability to everyday decisions, game fairness, risk assessment, and simulations that estimate Pi.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Monte-Carlo方法在核科学技术中应用

该课程所讲授的Monte-Carlo计算方法是核反应堆物理、核探测技术等领域的优势计算方法和必要技术手段。通过本课程学习，可以加深研究生对该方法的了解和认识，启发科研创新思维。

• XuetangX
• Self-Paced
• Free Online Course

Introduction to Uncertainty Quantification

10 ratings at Coursera

Quantify uncertainty in physical and engineering systems: aleatory versus epistemic uncertainty, probability foundations, Monte Carlo propagation, Gaussian process and polynomial chaos surrogates, reliability and Bayesian inference.

• Coursera
• 4 weeks, 7 hours a week
• Self-Paced
• Paid Course

Random Processes

Describe random processes and fields through correlation and spectral density functions, estimate their parameters from data, and simulate realizations with Monte Carlo for reliability analysis.

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

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

École normale supérieure

Statistical Mechanics: Algorithms and Computations

274 ratings at Coursera

Learn classical and quantum statistical mechanics by writing Python programs: Monte Carlo sampling, hard disks, path integrals, Bose-Einstein condensation, the Ising model, and simulated annealing.

• Coursera
• 10 weeks, 2 hours a week
• Self-Paced
• Free Online Course

Career Certificate

Foundations of Probability and Statistics

369 ratings at Coursera

Build a mathematical foundation for data science and AI: quantify uncertainty with random variables, model systems with discrete-time Markov chains, and construct maximum likelihood estimators.

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

ExcelIsFun

Excel Business Analytics - Forecasting, Linear Programming, Simulation and More

3 ratings

Master Excel business analytics through forecasting, linear programming, Monte Carlo simulation, Power Query data transformation, and statistical modeling techniques.

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

Computational Bayesian Statistics for Data Science

Approximate posteriors when algebra fails: compute MAP estimates by gradient ascent, run rejection sampling, Gibbs and Metropolis-Hastings MCMC, and fit Bayesian models with Stan in R.

• Coursera
• 5 weeks, 5 hours a week
• Self-Paced
• Paid Course

Particle Filters (and Navigation)

Develop particle filters for strongly nonlinear state estimation: Monte-Carlo integration, importance density, sequential importance sampling, resampling, and an Octave implementation applied to indoor navigation.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course