Engineering AI Agents
BOOK
Initial notebooks expected Sept 2024
COURSES
Introduction to AI
AI for Robotics
Deep Learning for Computer Vision
DATA MINING - BEING PORTED
ABOUT ME
Logical Reasoning
Logical Reasoning
Symbolic representations and Logical Inference
Syllabus
Syllabus
Foundations
Rules, rule the world
AI Agents
The four approaches towards AI
Data Science 360
The Learning Problem
Linear Regression
Optimization Algorithms
Entropy
Maximum Likelihood Estimation of a marginal model
Maximum Likelihood Estimation of Gaussian Parameters
Maximum Likelihood (ML) Estimation of conditional models
Introduction to Classification
Logistic Regression
Deep Neural Networks
Introduction to Backpropagation
Backpropagation in Deep Neural Networks
Backpropagation DNN exercises
Fashion MNIST Case Study
Regularization in Deep Neural Networks
Regularization Workshop
Fusion of Statistical Learning Theory, Information Theory and Stochastic Optimization
Perception & Scene Understanding
Introduction to Convolutional Neural Networks
CNN Layers
CNN Example Architectures
Using convnets with small datasets
Visualizing what convnets learn
Feature Extraction via Residual Networks
Introduction to Scene Understanding
Object Detection
Object Detection and Semantic Segmentation Metrics
Region-CNN (RCNN) Object Detection
Fast and Faster RCNN Object Detection
Object Detection & Semantic Segmentation Workshop
Mask R-CNN Semantic Segmentation
Mask R-CNN Demo
Mask R-CNN - Inspect Training Data
Mask R-CNN - Inspect Trained Model
Mask R-CNN - Inspect Weights of a Trained Model
Detectron2 Beginner’s Tutorial
Introduction to Transfer Learning
Transfer Learning for Computer Vision Tutorial
Recursive State Estimation
Discrete Bayes Filter
Localization and Tracking
Kalman Filters
Large Language Models
Introduction to Recurrent Neural Networks (RNN)
Simple RNN
The Long Short-Term Memory (LSTM) Architecture
Time Series Prediction using RNNs
Introduction to NLP Pipelines
Tokenization
Word2Vec Embeddings
Word2Vec from scratch
Word2Vec Tensorflow Tutorial
Language Models
CNN Language Model
Simple RNN Language Model
LSTM Language Model from scratch
RNN-based Neural Machine Translation
Character-level recurrent sequence-to-sequence model
NMT Metrics - BLEU
Attention in RNN-based NMT
Transformers and Self-Attention
Single-head self-attention
Multi-head self-attention
Positional Embeddings
Logical Reasoning
Automated Reasoning
World Models
Logical Inference
Logical Agents
Planning without Interactions
Automated Planning
Planning Domain Definition Language (PDDL)
The Unified Planning Library
Logistics Planning in PDDL
Manufacrturing Robot Planning in PDDL
Planning with Search
Forward Search Algorithms
The A* Algorithm
Interactive Demo
Motion Planning for Autonomous Cars
Acting - Markov Decision Processes
Markov Decision Processes
Introduction to MDP
Bellman Expectation Backup
Policy Evaluation (Prediction)
Bellman Optimality Backup
Policy Improvement (Control)
MDP Dynamic Programming Algorithms
Policy Iteration
Value Iteration
MDP Workshop
Cleaning Robot - Deterministic MDP
Cleaning Robot - Stochastic MDP
The recycling robot.
Acting - Reinforcement Learning
Reinforcement Learning
Monte-Carlo Prediction
Temporal Difference (TD) Prediction
Model-free control
Generalized Policy Iteration
\(\epsilon\)
-greedy Monte-Carlo (MC) Control
The SARSA Algorithm
SARSA Gridworld Example
Math Background
Math for ML Textbook
Probability Basics
Linear Algebra for Machine Learning
Calculus
Resources
Your Programming Environment
Training Keras with the SLURM Scheduler
NYU JupyrterHub Environments
Submitting Your Assignment / Project
Learn Python
Assignments
Assignment 1a
Assignment 1b
Project
Categories
All
(4)
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.
No matching items
Back to top
