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Related lectures (29)

Page 3 of 3

Introduction to Machine Learning: Basics and Examples

Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Compliant Control for Robots: Impedance and Variable Stiffness

Explores compliant control for robots through impedance and variable stiffness, enabling safe and adaptive interactions with the environment.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Robot Learning and Control

Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.

Introduction: Neurobotics

Introduces Neurobotics, a study combining neuroscience, robotics, and AI to understand embodied autonomous neural systems.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Learning and Adaptive Control for Robots: SEDS & LPV-DS

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.