Example AI Topics

A systems approach to AI

As its evident from all existing approaches towards AI, multidisciplinary science that aims to create agents that can think and act humanly or rationally. This course starts…

AI Agents

We will cover the agent-environment interface, the rational, learning agent architectures and a practical example of a robotic agent.

Pantelis Monogioudis

Data Science 360

What are the disciplines that we need to cross fertilize to get a system that possesses intelligence?

Rules, rule the world

We provide a historical perspective on AI development and the role of rules in mission-critical systems.

Pantelis Monogioudis

The Learning Problem

The supervised learning problem statement.

The four approaches towards AI

A 5-min behavioral intelligence test, where an interrogator chats with the player and at the end it guesses if the conversation is with a human or with a programmed machine.…

Detectron2 Beginner’s Tutorial

Welcome to detectron2! This is the official colab tutorial of detectron2. Here, we will go through some basics usage of detectron2, including the following: * Run inference…

Introduction to Scene Understanding

In the previous chapters we have treated the perception subsystem mainly from starting the first principles that govern supervised learning to the deep learning…

Mask R-CNN - Inspect Trained Model

Code and visualizations to test, debug, and evaluate the Mask R-CNN model.

Mask R-CNN - Inspect Training Data

Inspect and visualize data loading and pre-processing code.

Mask R-CNN - Inspect Weights of a Trained Model

This notebook includes code and visualizations to test, debug, and evaluate the Mask R-CNN model.

Mask R-CNN Demo

A quick intro to using the pre-trained model to detect and segment objects.

Attention in RNN-based NMT

When you hear the sentence “the soccer ball is on the field,” you don’t assign the same importance to all 7 words. You primarily take note of the words “ball” “on,” and “field…

CNN Language Model

The following was developed by Harini Appansrinivasan, NYU as part of an assignment submission.

Character-level recurrent sequence-to-sequence model

Author: fchollet
Date created: 2017/09/29
Last modified: 2020/04/26
Description: Character-level recurrent sequence-to-sequence model.

Finetuning Language Models for Text Classification - Patent Dataset

This notebook was submitted by NYU student Sky Achitoff

Pantelis Monogioudis

Introduction to NLP Pipelines

In this chapter, we will introduce the topic of processing with neural architectures language in general. This includes natural language, code etc.

LSTM Language Model from scratch

This notebook was borrowed from Christina Kouridis’ github. The notation is different than the notation used in the LTSM section of the notes and will be changed in a next…

Automated Reasoning

We have seen in an earlier chapter where we introduced a dynamical system governing the state evolution of the environment that a state is composed of variables and such fact…

Logical Agents

In this chapter we see how agents equipped with the ability to represent internally the state of the environment and reason about the effectiveness of possible actions using…

Logical Inference

The wumpus world despite its triviality, contains some deeper abstractions that are worth summarizing.

World Models

For each problem we can define a number of world models each representing every possible state (configuration) of the environment that the agent may be in.

Automated Planning

Planning combines two major areas of AI: logic and search.

Forward Search Algorithms

If you are missing some algorithmic background, afraid not. There is a free and excellent book to help you with the background behind this chapter. Read Chapters 3 and 4 for…

Interactive Demo

This demo is instructive of the various search algorithms we will cover here. You can introduce using your mouse obstacles in the canvas and see how the various search…

Logistics Planning in PDDL

We will use the Logistics domain to illustrate how to represent a planning task in PDDL.

Manufacrturing Robot Planning in PDDL

This is a real case that we tackled for a manufacturing company. This company devises supply chains to make pieces of medical equipments. A supply chain consists of…

Planning Domain Definition Language (PDDL)

In the chapter of propositional logic we have seen the combinatorial explosion problem that results from the need to include in the reasoning / inference step all possible…

Bellman Expectation Backup

In this section we describe how to calculate the value functions by establishing a recursive relationship similar to the one we did for the return. We replace the…

Bellman Optimality Backup

Now that we can calculate the value functions efficiently via the Bellman expectation recursions, we can now solve the MDP which requires maximize either of the two…

Cleaning Robot - Deterministic MDP

Cleaning Robot - Stochastic MDP

The following code shows the estimation of the q value function for a policy, the optimal q_star and the optimal policy for the cleaning robot problem in the strochastic case.

Finding optimal policies in Gridworld

Introduction to MDP

We start by reviewing the agent-environment interface with this evolved notation and provide additional definitions that will help in grasping the concepts behind DRL. We…

\(\epsilon\)-greedy Monte-Carlo (MC) Control

In this section we outline methods that can result in optimal policies when the MDP is unknown and we need to learn its underlying functions / models - also known as the mode…

Example of \(Q(s,a)\) Prediction

Suppose an agent is learning to play the toy environment shown above. This is a essentially a corridor and the agent has to learn to navigate to the end of the corridor to…

Generalized Policy Iteration

As we saw in the dynamic programming (DP) solution MDP problem, policy iteration is an algorithm that consists of two simultaneous, interacting processes: one making the…

Model-free control

Monte-Carlo Prediction

In this chapter we find optimal policy solutions when the MDP is unknown and we need to learn its underlying value functions - also known as the model free prediction…

Policy Gradient Algorithms - REINFORCE

Given that RL can be posed as an MDP, in this section we continue with a policy-based algorithm that learns the policy directly by optimizing the objective function and can…