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/Foundations: Statistical Learning Theory/Maximum Likelihood Estimation

Maximum Likelihood Estimation

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