Course Introduction

  • The Robotic AI Agent
  • Development Environment Setup
  • Mathematical Prerequisites

Foundations: Statistical Learning Theory

  • The learning problem
  • Linear Regression
  • Gradient Descent
  • Entropy
  • Maximum Likelihood Estimation
  • Binary Classification

Neural Networks

  • Feature extraction
  • Multiclass Classifier
  • Backpropagation
  • Regularization

Convolutional Neural Networks

  • How we understand scenes ?
  • Convolution and Correlation
  • CNN Architectures
  • Image Classification
  • What CNNs Learn
  • ResNets

Object Detection

  • Introduction to Object Detection.
  • Computer vision datasets
  • Region-based Object Detectors

Recursive State Estimation

  • Introduction to State Estimation with HMM.
  • The Bayes Filter
  • An example of Discrete Bayes Filter
  • Continuous state space and the Kalman filter
  • A Kalman filter example

Global Planning

  • Introduction to Planning.
  • Planning Domain Definition Language
  • Forward Search Algorithms
  • The A* Algorithm
AI for Robotics/Recursive State Estimation/A Kalman filter example

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