AI in Robotics

Pantelis Monogioudis, Ph.D

Course overview

A comprehensive journey in Robotics through the lens of AI.

2 modules
8 lessons
1 hr 58 min
Start the course
Module 1

Course Introduction

Robotic agents that can perceive and act in the world using AI.

  1. The Robotic AI Agent

    A practical map for navigating the illusion that you actually have any agency at all.

    14:36
  2. Mathematical Prerequisites

    What you need to know before diving into the course material.

    14:36
Module 2

Foundations: Statistical Learning Theory

From Vapnik to Hinton, the mathematical underpinnings of modern AI.

  1. The learning problem

    The Vapnik block diagram.

    14:52
  2. Linear Regression

    Extracting non-linear patterns with linear models.

    14:52
  3. Gradient Descent

    Optimizing complicated functions with iterative methods.

    14:52
  4. Entropy

    Informarion theory principles.

    14:52
  5. Maximum Likelihood Estimation

    The workhorse of statistical modeling.

    14:52
  6. Binary Classification

    Binary classification.

    14:52