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

Multimodal Reasoning and Transformers

  • Introduction to Transformers for language modeling.
  • The Learnable Attention Mechanism.
  • Multi-Head Self Attention
  • ViT Transformers and CLIP - Part 1

Markov Decision Processes

  • Introduction to MDPs - Part 1
  • Introduction to MDPs - Part 2
  • The Bellman Expectation Equations - Part 1
  • The Bellman Expectation Equations - Part 2
  • Policy Evaluation - Part 1
  • Policy Evaluation - Part 2
  • Bellman Optimal Value Functions
  • Policy Iteration and Value Iteration
AI for Robotics/Markov Decision Processes/The Bellman Expectation Equations - Part 1

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