Function Approximation Courses and Certifications

University of Alberta;  Alberta Machine Intelligence Institute

Prediction and Control with Function Approximation

23 reviews

Explore advanced reinforcement learning techniques for large state spaces, including function approximation, feature construction, and policy gradient methods. Apply these concepts to solve continuous-state control tasks.

• Coursera
• 21 hours 51 minutes
• On-Demand
• Paid Course

Alan Turing Institute

Approximation of Generalized Ridge Functions in High Dimensions – Sandra Keiper

Explores mathematical approaches to reconstruct complex processes in high dimensions, focusing on approximation theory and techniques to overcome the curse of dimensionality.

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

Simons Institute

What Are the Statistical Limits of Offline Reinforcement Learning With Function Approximation?

Explore statistical limitations in offline reinforcement learning with function approximation, covering realizability, coverage, policy evaluation, and practical considerations.

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

BIMSA

Deep Generative Models for Uncertainty Quantification in Function Approximation and PDE Problems

Explore deep generative models for quantifying uncertainty in function approximation and solving PDE problems with advanced mathematical techniques.

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

The Julia Programming Language

Rational Function Approximation in Julia

Discover rational function approximation techniques and implementations using Julia programming language for numerical computing applications.

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

Numerical Methods in EXCEL/VBA PROGRAMING

96 ratings at Udemy

Learn the workings of the most common numerical methods and a step by step process on how to program each of them

• Udemy
• 9 hours 44 minutes
• On-Demand
• Paid Course

MathWorks

Reinforcement Learning

Explore reinforcement learning fundamentals, neural networks for function representation, and how agents use environmental rewards to train systems for complex control problems.

• Coursera
• 2 hours 14 minutes
• On-Demand
• Paid Course

Calculus 1C: Coordinate Systems & Infinite Series

Dive into coordinate systems, curve calculus, and infinite series, mastering Taylor polynomials and convergence properties in this MIT calculus course.

• MITx Online
• 6 weeks, 5-10 hours a week
• On-Demand
• Free Online Course

Stanford CS234 - Reinforcement Learning - Spring 2024

Master reinforcement learning fundamentals through Stanford's comprehensive program covering MDP planning, policy evaluation, Q-learning, exploration strategies, and deep RL applications.

• YouTube
• 20 hours 11 minutes
• On-Demand
• Free Video

3Blue1Brown

Taylor Series

Explore the power of Taylor polynomials for approximations and analysis, covering cos(x), e^x, geometric interpretations, and convergence issues in this animated math lesson.

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

Simons Institute

Deterministic Approximation of the Ising Partition Function

Explore advanced techniques for approximating the Ising partition function, focusing on deterministic methods and their applications in statistical physics and computational complexity.

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

Institute for Advanced Study

Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin

Explore efficient reinforcement learning with linear function approximation, covering key concepts like sample efficiency, value-based algorithms, and exploration in multi-armed bandits and linear MDPs.

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

Simons Institute

On the Hardness of Reinforcement Learning With Value-Function Approximation

Explore challenges in reinforcement learning with value-function approximation, focusing on batch learning in large MDPs and the limitations of realizability assumptions.

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

GERAD Research Center

Neural Heuristics for Mathematical Optimization via Value Function Approximation

Explore neural heuristics for mathematical optimization using value function approximation. Learn about applications in stochastic, bilevel, and robust optimization, with insights on solution quality and efficiency.

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

Simons Institute

Instance Optimal Iterative Methods for Matrix Function Approximation

Explores instance-optimality of Lanczos method for matrix function approximation, extending conjugate gradient analysis to rational and other function classes. Discusses recent progress and open questions.

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