All Notes

Published

Friday, December 20, 2024

 
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Oren Bochman
Friday, December 20, 2024
 
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Oren Bochman
Friday, December 20, 2024
 
Pre-requisite self assessment
Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.
Oren Bochman
Friday, December 20, 2024
 
Pricing Theory
When it comes to pricing there are a number of pricing strategies 1 that can be used. These include:
Oren Bochman
Friday, December 20, 2024

Practical Time Series Analysis
I took this course on Coursera in primarily to get a more solid foundation in time series analysis and forecasting before taking the bayesian time series course. I have some…
Oren Bochman
Friday, December 20, 2024
 
Quiz App with ShinyLive 
questions = [
    {
        "question": "What is the capital of France?",
        "choices": ["Paris", "Rome", "Berlin", "Madrid"],
        "answer": "Paris"
    },
    {
 …
Oren Bochman
Friday, December 20, 2024
 
Fantasy Bayesian Course Catalog
There are no additional courses in the specialization at this time! However if I was asked what I wanted to learn next I would probably think of the following Idealy finding…
Oren Bochman
Friday, December 20, 2024
 
Bayesian Optimization
Bayesian Optimization is a powerful strategy for optimizing complex, expensive, or black-box functions, particularly prevalent in the field of machine learning for tasks…
Oren Bochman
Wednesday, December 4, 2024

Week 1: Introductions to time series analysis and the AR(1) process
The AR(1) process, Stationarity, ACF, PACF, Differencing, and Smoothing
Oren Bochman
Sunday, October 27, 2024

Week 4: Normal Dynamic Linear Models, Part 2
The AR(1) process, Stationarity, ACF, PACF, Differencing, and Smoothing
Oren Bochman
Saturday, October 26, 2024

Week 3: Normal Dynamic Linear Models, Part 1
Normal Dynamic Linear Models, Part 1
Oren Bochman
Friday, October 25, 2024

Week 2: The AR(p) process
The AR(1) process, Stationarity, ACF, PACF, Differencing, and Smoothing
Oren Bochman
Thursday, October 24, 2024

Mixture Models
…
Oren Bochman
Wednesday, October 23, 2024

Introductions to Time Series analysis & the AR(1) process
In the first week of the fourth course of Coursera’s ‘Bayesian Statistics Specialization’ we will define the AR(1) process, Stationarity, ACF, PACF, differencing, smoothing
Oren Bochman
Wednesday, October 23, 2024

Week 0: Introductions to time series analysis and the AR(1) process
The AR(1) process, Stationarity, ACF, PACF, Differencing, and Smoothing
Oren Bochman
Tuesday, October 22, 2024

Week 0: Feyman Notebook on Bayesian Time Series Analysis
Notes on Bayesian Time Series Analysis
Oren Bochman
Tuesday, October 22, 2024

Southern Oscillation Index
https://www.ncei.noaa.gov/access/monitoring/enso/soi/
Oren Bochman
Sunday, September 1, 2024

Policy Gradient
Are tasks really ever continuing? Everything eventually breaks or dies. It’s clear that individual people do not learn from death, but we don’t live forever. Why might the…
Oren Bochman
Thursday, April 4, 2024

Control with Approximation
In this video Adam White discusses the Episodic SARSA algorithm with function approximation. He explains how this algorithm can be used to solve reinforcement learning…
Oren Bochman
Wednesday, April 3, 2024

Constructing Features for Prediction
This is not a video lecture or notes for a learning goal. This is however my attempts to cover some material from the readings from chapter 9 of (Sutton and Barto 2018) menti…
Oren Bochman
Tuesday, April 2, 2024

Constructing Features for Prediction
We discussed methods for representing large, an possibly continuous state spaces. Ways to construct features. A representation is an agent’s internal encoding of the state…
Oren Bochman
Tuesday, April 2, 2024

On-Policy Prediction with Approximation
Some of the notes I made in this course became a bit too long. Rather than break the flow of the lesson I decided to move them to a separate file. This is one of those notes.
Oren Bochman
Monday, April 1, 2024

On-Policy Prediction with Approximation
I did not find the derivation of the SGD alg particularly enlightening and I have seen it several times. However the online setting is the best motivation for the use of SGD…
Oren Bochman
Monday, April 1, 2024

Sample-based Learning Methods
In these module we define cover model based RL sampling. We start with the Dyna architecture. Then we consider tabular Q-planning algorithm, the Tabular Dyna-Q and Dyna-Q+…
Oren Bochman
Monday, March 4, 2024

Temporal Difference Learning Methods for Control
This week, we will learn to using TD learning for control, as a generalized policy iteration strategy. We will see three different algorithms based on bootstrapping and…
Oren Bochman
Sunday, March 3, 2024

Temporal Difference Learning Methods for Prediction
In these unit we define some key terms like rewards, states, action, value functions, action values functions. Then we consider at the the multi-armed bandit problem leading…
Oren Bochman
Saturday, March 2, 2024

Monte-Carlo Methods for Prediction & Control
In this module we learn about Sample based MC methods that allow learning from sampled episodes. We revise our initial algorithm to better handle exploration. In off policy…
Oren Bochman
Friday, March 1, 2024
 
Rhetoric Bibliography
(Corbett and Connors 1999), (Engell 1999), (Torricelli and Carroll 2000), (Lucas 2011), (Farrell 1993)
Oren Bochman
Sunday, February 11, 2024
 
I Have a Dream speach
I am happy to join with you today in what will go down in history as the greatest demonstration for freedom in the history of our nation.1
Oren Bochman
Sunday, February 11, 2024

Urn model

Lesson 13 - Path dependence
Path dependence means that what happens at time T depends on all time steps t<T. Or put plainly that history matters.
Oren Bochman
Sunday, August 13, 2023

Culture dimensions

Lesson 12 - Coordination and Culture
Coordination is doing the same somebody else is doing. Culture refers to differences between groups of interacting people. In order to have these differences between groups…
Oren Bochman
Saturday, August 12, 2023

Trade

Lesson 11 - Lyapunov Functions
Lyapunov functions map models into outcomes. We can take a model or a system and ask whether there is a Lyapunov function that describes that model or system. If that is…
Oren Bochman
Friday, August 11, 2023

Historical trend of freedom in countries

Lesson 10 - Markov Processes
Markov models consist of entities that can be in a set of states and there are transition probabilities between those states. e.g., there is a set of students. Those…
Oren Bochman
Thursday, August 10, 2023

Landscape

Lesson 9 - Diversity and innovation
There are several issues regarding to problem solving, e.g. how individuals and teams go about solving problems, the role of diversity in problem solving, how ideas can get…
Oren Bochman
Wednesday, August 9, 2023

Comparison Botswana versus Zimbabwe

Lesson 8 - Economic Growth
Exponential growth is accumulating over time like interest. It is possible to make primitive models for economic growth. These models show that without innovation, growth…
Oren Bochman
Tuesday, August 8, 2023

Height of the camel

Lesson 7 - Tipping Points
The straw that broke the Camel’s back is utterly ridiculous example. Here is perhaps a few better ones:
Oren Bochman
Monday, August 7, 2023

Constructing linear model from data

Lesson 6 - Categorical and Linear Models
Categorical models allocates the data into different boxes. For example, trying to understand why some people live longer than others, we could divide them into people who…
Oren Bochman
Sunday, August 6, 2023

thinking fast and slow

Lesson 5 - Thinking Electrons Modelling People
“Imagine how hard physics would be if electrons could think.” — Murray Gell-Mann (1969 Nobel Laureate in Physics)
Oren Bochman
Saturday, August 5, 2023

Trains Tickets Decision tree

Lesson 4 - Decision Models
Decision models encode how people make decisions as well as how they should make decisions.
Oren Bochman
Friday, August 4, 2023

3D model hydrogen bonds in water source

Lesson 3 - Aggregation
More is different. — Physics Nobel Laureate Phillip Anderson, Nobel Memorial Prize Laureate
Oren Bochman
Thursday, August 3, 2023

Lesson 2 - Segregation and Peer Effects
In this lesson we get started by looking
Oren Bochman
Wednesday, August 2, 2023

Models Thinking?

Lesson 1 - Why Model?
Perhaps not the subject of this course, but sometimes I wonder
Oren Bochman
Tuesday, August 1, 2023

Lecture 5 — Explainable AI in practice
How to properly incorporate explanations in machine learning projects and what aspects should you keep in mind? Over the past few years the need to explain the output of…
Oren Bochman
Tuesday, March 28, 2023

4 Counterfactual Explanations - Explaining and Debugging
How to explain a machine learning model such that the explanation is truthful to the model and yet interpretable to people? This question is key to ML explanations research…
Oren Bochman
Thursday, March 23, 2023

3 Explaining Deep Neural Network Models
Rigorously understanding how Deep Learning models function may allow us to steer and control their behavior and avoid unwanted consequences. In addition, today’s highly…
Oren Bochman
Sunday, March 19, 2023

2 Local Explanations - Concept and Methods
Machine learning models can be analyzed at a high level using global explanations, such as linear model coefficients. However, there are several limitations to these global…
Oren Bochman
Monday, March 13, 2023

Introduction to XAI
In this introduction lecture on explainability in AI, we will delve into the key topics that surround this emerging field. We will first provide an overview of the…
Oren Bochman
Sunday, March 5, 2023

Lesson 1 - Overview of A/B Testing
Notes from Udacity A/B Testing course
Oren Bochman
Sunday, January 1, 2023

Lesson 2 - Policy and Ethics for Experiments
Notes from Udacity A/B Testing course
Oren Bochman
Sunday, January 1, 2023

Lesson 3 - Choosing and Characterizing Metrics
Notes from Udacity A/B Testing course
Oren Bochman
Sunday, January 1, 2023

Lesson 4 - Designing an experiment
Notes from Udacity A/B Testing course
Oren Bochman
Sunday, January 1, 2023

Lesson 5 - Analyzing Results
Notes from Udacity A/B Testing course
Oren Bochman
Sunday, January 1, 2023

Dynamic Programming
In week 4 we learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions…
Oren Bochman
Thursday, May 5, 2022

Value Functions & Bellman Equations
In week 3 we learn about Value Functions and Bellman Equations, which are the key technology behind all the algorithms we will learn. We learn the definition of policies and…
Oren Bochman
Wednesday, May 4, 2022

Markov Decision Processes
In week 2 we learn about Markov Decision Processes (MDP) and how to compute value functions and optimal policies, assuming you have the MDP model. We implement dynamic…
Oren Bochman
Tuesday, May 3, 2022

The K-Armed Bandit Problem
In week 1 we define some key concepts like rewards, states, action, value functions, action values functions. We consider the the multi-armed bandit problem, leading to…
Oren Bochman
Monday, May 2, 2022

Course Introduction
In week 1 we define some key concepts like rewards, states, action, value functions, action values functions. We consider the the multi-armed bandit problem, leading to…
Oren Bochman
Sunday, May 1, 2022

TensorFlow probability Independent
These are my course notes on Probabilistic Deep Learning with TensorFlow by Dr Kevin Webster of Imperial College London 2
Oren Bochman
Tuesday, June 1, 2021

TensorFlow probability Independent
Course notes for Probabilistic Deep Learning with TensorFlow by Dr Kevin Webster
Oren Bochman
Tuesday, June 1, 2021

Probabilistic Deep Learning with TensorFlow
Course notes for ‘Probabilistic Deep Learning with TensorFlow’ by Dr Kevin Webster
Oren Bochman
Tuesday, June 1, 2021

TensorFlow probability
These are my course notes on Probabilistic Deep Learning with TensorFlow by Dr Kevin Webster of Imperial College London 2
Oren Bochman
Tuesday, June 1, 2021
 
TensorFlow probability
These are my course notes on Probabilistic Deep Learning with TensorFlow by Dr Kevin Webster of Imperial College London 2 The course url
Oren Bochman
Tuesday, June 1, 2021

deeplearning.ai

Week 4 Chat Bots
This week of the NLP Specialization, we explore the world of Chatbots. We will be building Reformer model, an efficent transformer to create intelligent conversational…
Oren Bochman
Tuesday, April 27, 2021

tl-features-vs-fine-tuning

Week 3 Question Answering
This week of the NLP Specialization, we will uncover the field of Neural Question Answering. We will build advanced models like T5 and BERT to accurately answer questions…
Oren Bochman
Wednesday, April 14, 2021

Week 2 Text Summarization
This week of the NLP Specialization, we unlock the secrets of Neural Text Summarization. We will building a powerful Transformer model to extract crucial information and…
Oren Bochman
Wednesday, March 31, 2021

Week 1 Neural Machine Translation
This week of the Specialization, we’ll dive into the fundamentals of Neural Machine Translation (NMT). We’ll learn about the encoder-decoder architecture, which forms the…
Oren Bochman
Saturday, March 20, 2021

course banner

Preprocessing
In this lab, we will be exploring how to preprocess tweets for sentiment analysis. We will provide a function for preprocessing tweets during this week’s assignment, but it…
Oren Bochman
Friday, October 23, 2020

course banner

Building and Visualizing word frequencies
In this lab, we will focus on the build_freqs() helper functions and visualization of a dataset fed into it. In our goal of tweet sentiment analysis, this function will…
Oren Bochman
Friday, October 23, 2020

course banner

Visualizing tweets and the Logistic Regression model
Objectives: Visualize and interpret the logistic regression model Steps: * Plot tweets in a scatter plot using their positive and negative sums. * Plot the output of the…
Oren Bochman
Friday, October 23, 2020

course banner

Assignment 1 Logistic Regression
Welcome to week one of this specialization. You will learn about logistic regression. Concretely, you will be implementing logistic regression for sentiment analysis on…
Oren Bochman
Friday, October 23, 2020

Classification & Vector Spaces - Logistic Regression
Concepts, code snippets, slide commentaries for this week’s lesson of the Course notes from the deeplearning.ai natural language programming specialization.
Oren Bochman
Friday, October 23, 2020

Classification & Vector Spaces - Probability and Bayes’ Rule
Concepts, code snippets, and slide commentaries for this week’s lesson of the Course notes from the deeplearning.ai natural language programming specialization.
Oren Bochman
Friday, October 23, 2020

Classification & Vector Spaces - Probability and Bayes’ Rule
Concepts, code snippets, and slide commentaries for this week’s lesson of the Course notes from the deeplearning.ai natural language programming specialization.
Oren Bochman
Friday, October 23, 2020

Classification & Vector Spaces - Word Embeddings and Vector Spaces
Concepts, code snippets, and slide commentaries for this week’s lesson of the Course notes from the deeplearning.ai natural language programming specialization.
Oren Bochman
Friday, October 23, 2020
 
Deep Neural Networks - Some Questions
Unresolved questions on Deep learning.
Oren Bochman
Thursday, December 21, 2017

Combining RBMs into a DBM

Deep Neural Networks - Notes for Lesson 16
Notes for Deep learning focusing on the basics
Oren Bochman
Sunday, December 10, 2017
 
Deep Neural Networks - Notes for Lesson 15
Notes for Deep learning focusing on the basics
Oren Bochman
Friday, December 1, 2017

RDBM Combo

Deep Neural Networks - Notes for Lesson 14
Notes for Deep learning focusing on the basics
Oren Bochman
Tuesday, November 21, 2017

Sigmoid belief net

Deep Neural Networks - Notes for Lesson 13
The ups and downs of backpropagation
Oren Bochman
Saturday, November 11, 2017
 
Deep Neural Networks - Notes for Lesson 12
This module deals with Boltzmann machine learning
Oren Bochman
Wednesday, November 1, 2017
 
Deep Neural Networks - Notes for Lesson 11
This module deals with Boltzmann machine learning
Oren Bochman
Saturday, October 21, 2017
 
Deep Neural Networks — Readings II for Lesson 10
Review & summary of — Improving neural networks by preventing co-adaptation of feature detectors
Oren Bochman
Tuesday, October 3, 2017

ensemble architecture

Deep Neural Networks — Readings I for Lesson 10
Review & summary of — Evaluation of Adaptive Mixtures of Competing Experts
Oren Bochman
Monday, October 2, 2017

Deep Neural Networks - Notes for Lesson 10
This module we look at why it helps to combine multiple NN to improve generalization
Oren Bochman
Sunday, October 1, 2017

Deep Neural Networks - Notes for Lesson 9
We discuss strategies to make neural networks generalize better
Oren Bochman
Thursday, September 21, 2017

Curvature Matrices

Deep Neural Networks - Notes for Lesson 8
We continue our look at recurrent neural networks
Oren Bochman
Monday, September 11, 2017

Deep Neural Networks - Notes for Lesson 7e
Training RNNs with back propagation
Oren Bochman
Wednesday, September 6, 2017

Deep Neural Networks - Notes for Lesson 7d
Why it is difficult to train an RNN
Oren Bochman
Tuesday, September 5, 2017

toy RNN problem

Deep Neural Networks - Notes for Lesson 7c
A toy example of training an RNN
Oren Bochman
Monday, September 4, 2017

Deep Neural Networks - Notes for Lesson 7b
Training RNNs with back propagation
Oren Bochman
Sunday, September 3, 2017

Memoryless models

Deep Neural Networks - Notes for Lesson 7a
Modeling sequences — A brief overview
Oren Bochman
Saturday, September 2, 2017

Memoryless models

Deep Neural Networks - Notes for Lesson 7
This module explores training recurrent neural networks
Oren Bochman
Friday, September 1, 2017

some learning rate algorithms

Deep Neural Networks - Notes for lecture 6e
rmsprop - divide the gradient by a running average of its recent magnitude
Oren Bochman
Monday, August 28, 2017

Deep Neural Networks - Notes for lecture 6d
Adaptive learning rates for each connection
Oren Bochman
Sunday, August 27, 2017

Momentum intution

Deep Neural Networks - Notes for lecture 6c
The momentum method
Oren Bochman
Saturday, August 26, 2017

Deep Neural Networks - Notes for lecture 6b
A bag of tricks for mini-batch gradient descent
Oren Bochman
Friday, August 25, 2017

error surface

Deep Neural Networks - Notes for lecture 6a
Overview of mini-batch gradient descent
Oren Bochman
Thursday, August 24, 2017
 
Deep Neural Networks - Notes for Lesson 6
We delve into mini-batch gradient descent as well as discuss adaptive learning rates.
Oren Bochman
Wednesday, August 23, 2017

Examples from the test set

Deep Neural Networks - Notes for lecture 5d
Why object recognition is difficult
Oren Bochman
Monday, August 21, 2017

Deep Neural Networks - Notes for lecture 5c
Convolutional neural networks for hand-written digit recognition
Oren Bochman
Sunday, August 20, 2017
 
Deep Neural Networks - Notes for lecture 5b
Ways to achieve viewpoint invariance
Oren Bochman
Saturday, August 19, 2017

Deep Neural Networks - Notes for lecture 5a
Why object recognition is difficult
Oren Bochman
Friday, August 18, 2017

Deep Neural Networks - Notes for Lesson 5
Notes on Deep leaning and ML from Neural Networks for Machine Learning by Geoffrey Hintonon on Coursera
Oren Bochman
Thursday, August 17, 2017

A serial architecture

Deep Neural Networks - Notes for lecture 4e
Ways to deal with the large number of possible outputs
Oren Bochman
Tuesday, August 15, 2017
 
Deep Neural Networks - Notes for lecture 4d
Neuro-probabilistic language models
Oren Bochman
Monday, August 14, 2017

Deep Neural Networks - Notes for lecture 4c
Another diversion — The Softmax output function
Oren Bochman
Sunday, August 13, 2017
 
Deep Neural Networks - Notes for lecture 4b
A brief diversion into cognitive science
Oren Bochman
Saturday, August 12, 2017

relational information

Deep Neural Networks - Notes for lecture 4a
Learning to predict the next word
Oren Bochman
Friday, August 11, 2017

relational information

Deep Neural Networks - Notes for Lesson 4
Notes on Deep leaning and ML from Neural Networks for Machine Learning by Geoffrey Hinton on Coursera
Oren Bochman
Thursday, August 10, 2017
 
Deep Neural Networks - Notes From Hinton’s Course
My thoughts are that we should be able to do better than this version of dropout. - Shortcoming: - Dropout on units can render the net very poor. - Drop out slows training…
Oren Bochman
Sunday, August 6, 2017
 
Glossary of terms for Deep Neural Networks
Glossary of terms in Deep leaning and ML from Neural Networks for Machine Learning by Geoffrey Hintonon on Coursera
Oren Bochman
Sunday, August 6, 2017

overfitting

Deep Neural Networks - Notes for lecture 3e
Using the derivatives computed by backpropagation
Oren Bochman
Sunday, August 6, 2017

back proogations of errors

Deep Neural Networks - Notes for lecture 3d
The back-propagation algorithm
Oren Bochman
Saturday, August 5, 2017

logistic activation function

Deep Neural Networks - Notes for lecture 3c
Learning the weights of a logistic output neuron
Oren Bochman
Friday, August 4, 2017

error surface of a linear neuron

Deep Neural Networks - Notes for lecture 3b
The error surface for a linear neuron
Oren Bochman
Thursday, August 3, 2017

the true weights

Deep Neural Networks - Notes for lecture 3a
Focusing on Learning the weights of a linear neuron
Oren Bochman
Wednesday, August 2, 2017

the true weights

Deep Neural Networks - Notes for Lesson 3
Notes on Deep leaning and ML from Neural Networks for Machine Learning by Geoffrey Hintonon on Coursera
Oren Bochman
Tuesday, August 1, 2017

Impossible to satisfy

Deep Neural Networks - Notes for lecture 2e
Notes for Deep learning focusing What Perceptrons can not do
Oren Bochman
Friday, July 21, 2017

idea of the proof

Deep Neural Networks - Notes for lecture 2d
Notes for Deep learning focusing on why the learning works?
Oren Bochman
Thursday, July 20, 2017

Weight-space

Deep Neural Networks - Notes for lecture 2c
Notes for Deep learning focusing on a geometrical view of perceptrons
Oren Bochman
Wednesday, July 19, 2017

The standard Perceptron architecture

Deep Neural Networks - Notes for lecture 2b
Notes for Deep learning focusing on Perceptrons, the first generation of neural networks.
Oren Bochman
Tuesday, July 18, 2017

Deep Neural Networks - Notes for lecture 2a
Notes for Deep learning focusing on types of neural network architectures
Oren Bochman
Monday, July 17, 2017

Feed-forward neural networks

Deep Neural Networks - Notes for Lesson 2
Notes for Deep learning focusing on Perceptrons
Oren Bochman
Sunday, July 16, 2017
 
Deep Neural Networks - Notes for lecture 1e
Notes for Deep learning focusing on the three types of learning
Oren Bochman
Thursday, July 6, 2017
 
Deep Neural Networks - Notes for lecture 1d
Notes for Deep learning focusing on a simple example of learning learning?
Oren Bochman
Wednesday, July 5, 2017

linear activation function

Deep Neural Networks - Notes for lecture 1c
Notes for Deep learning focusing on simple models of neurons
Oren Bochman
Tuesday, July 4, 2017
 
Deep Neural Networks - Notes for lecture 1b
Notes for Deep learning focusing on why do we need machine learning?
Oren Bochman
Monday, July 3, 2017

Drosophila melanogaster Proboscis

Deep Neural Networks - Notes for lecture 1a
Why do we need machine learning?
Oren Bochman
Sunday, July 2, 2017
 
Deep Neural Networks - Notes for Lesson 1
Notes for Deep learning focusing on the basics
Oren Bochman
Saturday, July 1, 2017

Lesson 26 Wrap-Up
Today we’ll wrap up this course. It’s been a fun journey. But like all journeys, this one too must come to an end. The goal for today’s lesson is to tie together some of the…
Oren Bochman
Saturday, February 20, 2016

Lesson 25 Advanced Topics
To close this class, we will discuss a handful of advanced topics, related to the course material.
Oren Bochman
Friday, February 19, 2016

Lesson 24 Meta-Reasoning
In this lesson we shall explore the concept of Meta-reasoning which is the ability to think about one’s own thinking processes. It’s essentially reasoning about reasoning.
Oren Bochman
Thursday, February 18, 2016

Lesson 23 Learning by Correcting Mistakes
In this lesson we cover another method of learning, called learning by correcting mistakes.
Oren Bochman
Wednesday, February 17, 2016

Lesson 22 Diagnosis
In this lesson we cover diagnosis, which is the identification of the fault or faults responsible for a malfunctioning system.
Oren Bochman
Tuesday, February 16, 2016

Lesson 21 Configuration
In this lesson, we will discuss configuration. This is a a common design task for which all the components of the design are already known.
Oren Bochman
Monday, February 15, 2016

Lesson 20 Constraint Propagation
Today, we’ll talk about constraint propagation, another very drawn out purpose method. Constraint propagation is a mechanism of influence where the agent assign values to…
Oren Bochman
Sunday, February 14, 2016

Lesson 19 Version Spaces
In this lesson, we will discuss generalization and learning, using the method of version spaces.
Oren Bochman
Saturday, February 13, 2016

Lesson 18 Analogical Reasoning
In this lesson, we will discuss Analogical Reasoning. This involves understanding new problems, in terms of family of problems.
Oren Bochman
Friday, February 12, 2016

Lesson 17 Explanation-Based Learning
In this lesson, we will discuss explanation-based learning, in which the agent doesn’t learn about new concepts. Instead, it learns about connections among existing concepts.
Oren Bochman
Thursday, February 11, 2016

Lesson 16 Scripts
In this lesson, we will discuss another KR called scripts. These allow us to make sense of discourses and stories and scenes.
Oren Bochman
Wednesday, February 10, 2016

Lesson 15 Commonsense Reasoning
In this lesson, we will discuss common-sense reasoning. You and I, as cognitive individuals, are very good at common-sense reasoning.
Oren Bochman
Tuesday, February 9, 2016

Lesson 14 Understanding
In this lesson, we will discuss understanding which is a very big general world.
Oren Bochman
Monday, February 8, 2016

Lesson 13 Planning
Today we’ll talk about planning. Recall that we had said an intelligent agent maps perceptual histories into actions. Planning is a powerful method for action selection.…
Oren Bochman
Sunday, February 7, 2016

Lesson 12 Logic
In this lesson, we will discuss logic which is a formal language that allows us to make assertions about the world in a very precise way.
Oren Bochman
Saturday, February 6, 2016

Lesson 11 Classification
In this lesson we will study a ubiquitous problems in AI called classification. This is the mapping of sets of percepts in the world onto some equivalence classes which…
Oren Bochman
Friday, February 5, 2016

Lesson 10 Incremental Concept Learning
In this lesson we discuss incremental concept learning. In case based reasoning, we store new examples in memory and now consider how to abstract concepts out of those…
Oren Bochman
Thursday, February 4, 2016

Lesson 09 Case-Based Reasoning
In this lesson we will discuss Case-Based Reasoning. A cognitive agent uses Case-based reasoning to addresses new problems by tweaking solutions to similar previously…
Oren Bochman
Wednesday, February 3, 2016

Lesson 08 Learning by Recording Cases
In this lesson we will talk about learning by recording cases. This is our first topic of learning, one of the fundamental elements of knowledge-based AI.
Oren Bochman
Tuesday, February 2, 2016

Lesson 07 Frames
Today we’ll talk about frames. Frames are a very powerful and very common knowledge representation.
Oren Bochman
Monday, February 1, 2016

Lesson 06 Production Systems
In this lesson, we will discuss production systems. Production systems are kind of cognitive architecture, in which knowledge is represented in the form of rules.
Oren Bochman
Saturday, January 30, 2016

05 - Means-End Analysis
Today we will discuss two other very general AR methods of problem-solving called means end analysis and problem reduction.
Oren Bochman
Friday, January 29, 2016

Lesson 04 Generate & Test
We now focus on problem-solving methods.
Oren Bochman
Tuesday, January 26, 2016

Lesson 03 Semantic Networks
A large part of this lesson explores solving two types of problems. By using a graph to visually represent the process, we end up with a semantic network. However, it seems…
Oren Bochman
Monday, January 25, 2016

Lesson 02 Introduction to CS7637
In this lesson, we’ll talk most specifically about what to expect from CS7637.
Oren Bochman
Sunday, January 24, 2016

01 - Introduction to Knowledge-Based AI
The instructors consider this course as an experiment. They want to understand how students learn in online classrooms.
Oren Bochman
Saturday, January 23, 2016

01 - Course Material
  • knowledge-based AI Class Documents
  • Syllabus for the course
  • Full Calendar
Oren Bochman
Wednesday, January 20, 2016
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