Function Approximation Courses and Certifications

Centre for Networked Intelligence, IISc

Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation: Tail Averaging and Regularization

Delve into advanced temporal difference learning analysis, exploring tail-averaging techniques and regularization methods for improved convergence rates in reinforcement learning applications.

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

IIT Madras;  NPTEL

Reinforcement Learning

Explore mathematical foundations and recent research in trial-and-error learning, covering bandit algorithms, MDPs, dynamic programming, function approximation, and advanced topics like hierarchical RL and POMDPs.

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

Vidyasagar University;  CEC

Numerical Analysis

Comprehensive exploration of numerical methods for solving mathematical problems, covering interpolation, approximation, equation solving, matrix operations, differentiation, integration, and differential equations.

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

Professor Leonard

Calculus 2 - Approximation of Functions by Taylor Polynomials

Explore the approximation of functions using Taylor polynomials, a powerful mathematical tool for estimating complex functions with simpler polynomial expressions.

• YouTube
• 1 hour 34 minutes
• On-Demand
• Free Video

Professor Leonard

Calculus 2 - Representation of Functions by Taylor Series and Maclaurin Series

Explore Taylor and Maclaurin series for representing functions, enhancing your understanding of advanced calculus concepts and their practical applications.

• YouTube
• 3 hours 2 minutes
• On-Demand
• Free Video

Reinforcement Learning Course

Comprehensive exploration of reinforcement learning concepts, from basics to advanced topics, including deep RL, policy gradients, and practical applications in classic games.

• YouTube
• 17 hours
• On-Demand
• Free Video

HUJI Machine Learning Club

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation (Hebrew)

Explore Uri Sherman's research on Policy Mirror Descent convergence, introducing a variational gradient dominance condition that enables convergence for general policy classes without strong closure requirements.

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

Neural Breakdown with AVB

Kolmogorov Arnold Networks: An Alternative Paradigm for Deep Learning

Dive into an analysis of Kolmogorov Arnold Networks (KAN), exploring how this innovative approach combines the strengths of MLPs and splines to advance deep learning architecture and capabilities.

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

Simons Institute

Online Reinforcement Learning and Regret

Explore online learning in reinforcement learning, focusing on regret analysis under Markovian dynamics, function approximation, and structural challenges in unknown model scenarios.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Applied Algebraic Topology Network

On the Topological Expressiveness of Neural Networks

Explore the topological constraints of neural network architectures and their impact on function representation, with insights from recent research and ongoing work in the field.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

International Mathematical Union

Theory of Graph Neural Networks: Representation and Learning

Explore the theory behind Graph Neural Networks, covering approximation, learning properties, and connections to graph isomorphism, equivariant functions, and dynamic programming.

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

Institute for Pure & Applied Mathematics (IPAM)

On the Statistical Complexity of Reinforcement Learning

Explores statistical complexities in reinforcement learning, covering sample complexity, regret analysis, and off-policy evaluation. Discusses minimax-optimal approaches and function approximation in MDPs.

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

Jeff Heaton

Deep Neural Network Autoencoders in TensorFlow and Keras - Part 1

Learn to create and implement autoencoders in Keras and TensorFlow, including denoising autoencoders, for deep neural network applications in image processing and function approximation.

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

Simons Institute

Online Learning in Markov Decision Processes - Part 2

Explore advanced concepts in Markov Decision Processes, including adversarial scenarios, regret analysis, and algorithms for online learning in dynamic environments.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Institute for Advanced Study

The Challenges of Model-Based Reinforcement Learning and How to Overcome Them - Csaba Szepesvári

Explore challenges in model-based reinforcement learning, including modeling errors and efficient planning, and discover strategies to overcome them in complex environments.

• YouTube
• 1 hour 39 minutes
• On-Demand
• Free Video