Monte Carlo Methods Courses and Certifications

Decision Making and Reinforcement Learning

18 ratings at Coursera

Explore sequential decision-making and reinforcement learning, covering utility theory, bandit problems, MDPs, POMDPs, Monte Carlo methods, and temporal difference learning. Gain practical skills through algorithms and examples.

• Coursera
• 1 day 23 hours 9 minutes
• On-Demand
• Free Online Course (Audit)

Designing Larger Python Programs for Data Science

Learn to design complex Python programs for data science, covering top-down design, Monte Carlo simulations, and efficient code integration. Develop skills to handle large-scale projects and simulate real-world problems.

• Coursera
• 1 day 17 hours 57 minutes
• On-Demand
• Free Online Course (Audit)

Derivatives

Explore derivative pricing models, financial markets, and numerical methods. Master discrete and continuous time techniques, fixed income products, and Monte Carlo option pricing.

• Coursera
• 8 hours 9 minutes
• On-Demand
• Free Online Course (Audit)

Reinforcement Learning with Gymnasium in Python

Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Discrete Event Simulation in Python

Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Python's SimPy package.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Applied Bayesian for Analytics

Learn how to construct, fit, estimate and compute Bayesian statistical models with the help of OpenBUGS (freely available software)

• edX
• 6 weeks, 2-3 hours a week
• On-Demand
• Free Online Course (Audit)

Reinforcement Learning

This MOOC teaches the basics in reinforcement learning. It provides participants with a solid mathematical basis to study new methods from reinforcement learning research and put them into practice.

• edX
• 8 weeks, 2-3 hours a week
• On-Demand
• Free Online Course (Audit)

Reinforcement Learning Foundations

437 ratings at LinkedIn Learning

Learn the basics of reinforcement learning (RL), including the terminology, the kinds of problems you can solve with RL, and the different methods for solving those problems.

• LinkedIn Learning
• 44 minutes
• On-Demand
• Free Trial Available

IIT Guwahati;  NPTEL

Simulation of Communication Systems using Matlab

Learn to model and simulate communication systems using MATLAB, covering Monte Carlo methods, random processes, AWGN systems, fading channels, MIMO, Markov chains, queueing, and optimal filtering.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Implementing Monte Carlo Method in R

Some problems are difficult or impossible to solve with standard mathematical or statistical approaches - these can often be solved by using the Monte Carlo method. This course covers multiple Monte Carlo applications for ready use.

• Pluralsight
• 1-2 hours
• On-Demand
• Free Trial Available

Simons Institute

Simulation Methodology - An Overview

Explore simulation methodology, covering numerical integration, Monte Carlo methods, and output analysis. Learn key concepts and techniques for tackling high-dimensional problems in computational science.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

Open Data Science

An Intuition-Based Approach to Reinforcement Learning

Explore an intuition-based approach to reinforcement learning, covering key concepts like exploit vs. explore, Q-tables, and stochastic actions, with practical examples and applications.

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

Kavli Institute for Theoretical Physics

Effect of the Optical Response of Nanostructured Materials - Victoria Esteso Carrizo

Explores nanostructured materials' optical response and its impact on Casimir-Lifshitz force, covering multilayer structures, absorption, and interference effects in quantum electrodynamics.

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

Kavli Institute for Theoretical Physics

Post Processing and Mock Observations - Shane Davis, Philipp Moesta

Explore post-processing techniques and mock observations for binary systems, focusing on circumbinary gas evolution, observational signatures, and numerical modeling advancements.

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

Alan Turing Institute

Deep Learning Volatility - Blanka Horvath, Kings College London

Explore deep learning for option pricing and calibration, including neural network perspectives, rough volatility, and model recognition in financial mathematics.

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