From 4bdb70a0080269accd9c2025019dfb10bd46c6aa Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 25 Nov 2021 05:37:29 +0100 Subject: [PATCH] added some text to week 47 --- doc/pub/week47/html/week47-bs.html | 110 ++-- doc/pub/week47/html/week47-reveal.html | 59 ++- doc/pub/week47/html/week47-solarized.html | 69 ++- doc/pub/week47/html/week47.html | 69 ++- doc/pub/week47/ipynb/ipynb-week47-src.tar.gz | Bin 823750 -> 823750 bytes doc/pub/week47/ipynb/week47.ipynb | 524 +++++++++++-------- doc/src/week47/week47.do.txt | 50 ++ 7 files changed, 604 insertions(+), 277 deletions(-) diff --git a/doc/pub/week47/html/week47-bs.html b/doc/pub/week47/html/week47-bs.html index 79d233cbd..ea9e55a0f 100644 --- a/doc/pub/week47/html/week47-bs.html +++ b/doc/pub/week47/html/week47-bs.html @@ -99,6 +99,19 @@ doconce format html week47.do.txt --html_style=bootstrap --pygments_html_style=d 2, None, 'what-me-worry-no-final-exam-in-this-course'), + ('What is the link between Artificial Intelligence and Machine ' + 'Learning and some general Remarks', + 2, + None, + 'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'), + ('Going back to the beginning of the semester', + 2, + None, + 'going-back-to-the-beginning-of-the-semester'), + ('Not so sharp distinctions', + 2, + None, + 'not-so-sharp-distinctions'), ('Topics we have covered this year', 2, None, @@ -281,51 +294,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Summary of course
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • What is the link between Artificial Intelligence and Machine Learning and some general Remarks
    • +
  • Going back to the beginning of the semester
    • +
  • Not so sharp distinctions
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -355,7 +371,7 @@ MathJax.Hub.Config({
    -

    Nov 20, 2021

    +

    Nov 25, 2021


    @@ -380,7 +396,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 76
    • +
  • 79
  • »
    • diff --git a/doc/pub/week47/html/week47-reveal.html b/doc/pub/week47/html/week47-reveal.html index fdb88c131..008b67824 100644 --- a/doc/pub/week47/html/week47-reveal.html +++ b/doc/pub/week47/html/week47-reveal.html @@ -184,7 +184,7 @@ MathJax.Hub.Config({
    -

    Nov 20, 2021

    +

    Nov 25, 2021


    @@ -1601,6 +1601,63 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb

    +
    + + +

    Artificial intelligence is built upon integrated machine learning +algorithms as discussed in this course, which in turn are fundamentally rooted in optimization and +statistical learning. +

    + +

    Can we have Artificial Intelligence without Machine Learning? See this post for inspiration.

    +
    + +
    +

    Going back to the beginning of the semester

    + +

    Traditionally the field of machine learning has had its main focus on +predictions and correlations. These concepts outline in some sense +the difference between machine learning and what is normally called +Bayesian statistics or Bayesian inference. +

    + +

    In machine learning and prediction based tasks, we are often +interested in developing algorithms that are capable of learning +patterns from given data in an automated fashion, and then using these +learned patterns to make predictions or assessments of newly given +data. In many cases, our primary concern is the quality of the +predictions or assessments, and we are less concerned with the +underlying patterns that were learned in order to make these +predictions. This leads to what normally has been labeled as a +frequentist approach. +

    +
    + +
    +

    Not so sharp distinctions

    + +

    You should keep in mind that the division between a traditional +frequentist approach with focus on predictions and correlations only +and a Bayesian approach with an emphasis on estimations and +causations, is not that sharp. Machine learning can be frequentist +with ensemble methods (EMB) as examples and Bayesian with Gaussian +Processes as examples. +

    + +

    If one views ML from a statistical learning +perspective, one is then equally interested in estimating errors as +one is in finding correlations and making predictions. It is important +to keep in mind that the frequentist and Bayesian approaches differ +mainly in their interpretations of probability. In the frequentist +world, we can only assign probabilities to repeated random +phenomena. From the observations of these phenomena, we can infer the +probability of occurrence of a specific event. In Bayesian +statistics, we assign probabilities to specific events and the +probability represents the measure of belief/confidence for that +event. The belief can be updated in the light of new evidence. +

    +
    +

    Topics we have covered this year

    diff --git a/doc/pub/week47/html/week47-solarized.html b/doc/pub/week47/html/week47-solarized.html index b6401cbc1..19684e17b 100644 --- a/doc/pub/week47/html/week47-solarized.html +++ b/doc/pub/week47/html/week47-solarized.html @@ -126,6 +126,19 @@ div.toc p,a { 2, None, 'what-me-worry-no-final-exam-in-this-course'), + ('What is the link between Artificial Intelligence and Machine ' + 'Learning and some general Remarks', + 2, + None, + 'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'), + ('Going back to the beginning of the semester', + 2, + None, + 'going-back-to-the-beginning-of-the-semester'), + ('Not so sharp distinctions', + 2, + None, + 'not-so-sharp-distinctions'), ('Topics we have covered this year', 2, None, @@ -281,7 +294,7 @@ MathJax.Hub.Config({
    -

    Nov 20, 2021

    +

    Nov 25, 2021


    @@ -1528,6 +1541,60 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb

    +









    + + +

    Artificial intelligence is built upon integrated machine learning +algorithms as discussed in this course, which in turn are fundamentally rooted in optimization and +statistical learning. +

    + +

    Can we have Artificial Intelligence without Machine Learning? See this post for inspiration.

    + +









    +

    Going back to the beginning of the semester

    + +

    Traditionally the field of machine learning has had its main focus on +predictions and correlations. These concepts outline in some sense +the difference between machine learning and what is normally called +Bayesian statistics or Bayesian inference. +

    + +

    In machine learning and prediction based tasks, we are often +interested in developing algorithms that are capable of learning +patterns from given data in an automated fashion, and then using these +learned patterns to make predictions or assessments of newly given +data. In many cases, our primary concern is the quality of the +predictions or assessments, and we are less concerned with the +underlying patterns that were learned in order to make these +predictions. This leads to what normally has been labeled as a +frequentist approach. +

    + +









    +

    Not so sharp distinctions

    + +

    You should keep in mind that the division between a traditional +frequentist approach with focus on predictions and correlations only +and a Bayesian approach with an emphasis on estimations and +causations, is not that sharp. Machine learning can be frequentist +with ensemble methods (EMB) as examples and Bayesian with Gaussian +Processes as examples. +

    + +

    If one views ML from a statistical learning +perspective, one is then equally interested in estimating errors as +one is in finding correlations and making predictions. It is important +to keep in mind that the frequentist and Bayesian approaches differ +mainly in their interpretations of probability. In the frequentist +world, we can only assign probabilities to repeated random +phenomena. From the observations of these phenomena, we can infer the +probability of occurrence of a specific event. In Bayesian +statistics, we assign probabilities to specific events and the +probability represents the measure of belief/confidence for that +event. The belief can be updated in the light of new evidence. +

    +









    Topics we have covered this year

    diff --git a/doc/pub/week47/html/week47.html b/doc/pub/week47/html/week47.html index da6c188bc..79418794d 100644 --- a/doc/pub/week47/html/week47.html +++ b/doc/pub/week47/html/week47.html @@ -203,6 +203,19 @@ div.toc p,a { 2, None, 'what-me-worry-no-final-exam-in-this-course'), + ('What is the link between Artificial Intelligence and Machine ' + 'Learning and some general Remarks', + 2, + None, + 'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'), + ('Going back to the beginning of the semester', + 2, + None, + 'going-back-to-the-beginning-of-the-semester'), + ('Not so sharp distinctions', + 2, + None, + 'not-so-sharp-distinctions'), ('Topics we have covered this year', 2, None, @@ -358,7 +371,7 @@ MathJax.Hub.Config({
    -

    Nov 20, 2021

    +

    Nov 25, 2021


    @@ -1605,6 +1618,60 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb

    +









    + + +

    Artificial intelligence is built upon integrated machine learning +algorithms as discussed in this course, which in turn are fundamentally rooted in optimization and +statistical learning. +

    + +

    Can we have Artificial Intelligence without Machine Learning? See this post for inspiration.

    + +









    +

    Going back to the beginning of the semester

    + +

    Traditionally the field of machine learning has had its main focus on +predictions and correlations. These concepts outline in some sense +the difference between machine learning and what is normally called +Bayesian statistics or Bayesian inference. +

    + +

    In machine learning and prediction based tasks, we are often +interested in developing algorithms that are capable of learning +patterns from given data in an automated fashion, and then using these +learned patterns to make predictions or assessments of newly given +data. In many cases, our primary concern is the quality of the +predictions or assessments, and we are less concerned with the +underlying patterns that were learned in order to make these +predictions. This leads to what normally has been labeled as a +frequentist approach. +

    + +









    +

    Not so sharp distinctions

    + +

    You should keep in mind that the division between a traditional +frequentist approach with focus on predictions and correlations only +and a Bayesian approach with an emphasis on estimations and +causations, is not that sharp. Machine learning can be frequentist +with ensemble methods (EMB) as examples and Bayesian with Gaussian +Processes as examples. +

    + +

    If one views ML from a statistical learning +perspective, one is then equally interested in estimating errors as +one is in finding correlations and making predictions. It is important +to keep in mind that the frequentist and Bayesian approaches differ +mainly in their interpretations of probability. In the frequentist +world, we can only assign probabilities to repeated random +phenomena. From the observations of these phenomena, we can infer the +probability of occurrence of a specific event. In Bayesian +statistics, we assign probabilities to specific events and the +probability represents the measure of belief/confidence for that +event. The belief can be updated in the light of new evidence. +

    +









    Topics we have covered this year

    diff --git a/doc/pub/week47/ipynb/ipynb-week47-src.tar.gz b/doc/pub/week47/ipynb/ipynb-week47-src.tar.gz index 5744b113daab11ecbcb7ad0c52bf55bf5fd040e0..f5c93d0b9bb2b68641026d8eb635a6a70a11ee94 100644 GIT binary patch delta 54 zcmX@M*yz||BX;?24u+pX^BdV)*%@2enOfPITiID!*;!lJ*;?6wf*h^voUQC!t?b-e J*?IO)1OT_I4uAjv delta 54 zcmX@M*yz||BX;?24u*xrGaK1k*%@2enOfPITiID!*;!lJ*;?6wf*h^voUQC!t?b-e J*?IO)1OT_!4ub#y diff --git a/doc/pub/week47/ipynb/week47.ipynb b/doc/pub/week47/ipynb/week47.ipynb index 06a112f46..ac896dc25 100644 --- a/doc/pub/week47/ipynb/week47.ipynb +++ b/doc/pub/week47/ipynb/week47.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "328d1bac", + "id": "11eedad1", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "1204a257", + "id": "d229ce51", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Week 47: Support Vector Machines and Summary of Course\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Nov 20, 2021**\n", + "Date: **Nov 25, 2021**\n", "\n", "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "8cdcf51d", + "id": "05269c87", "metadata": { "editable": true }, @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "da8706d4", + "id": "835c09a2", "metadata": { "editable": true }, @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "53e81d94", + "id": "873bd806", "metadata": { "editable": true }, @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "40aa0e2b", + "id": "70acd877", "metadata": { "collapsed": false, "editable": true @@ -187,7 +187,7 @@ }, { "cell_type": "markdown", - "id": "ce54e004", + "id": "037c53af", "metadata": { "editable": true }, @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "7583748e", + "id": "6937fa1a", "metadata": { "editable": true }, @@ -219,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "507c1b1e", + "id": "5a04f85f", "metadata": { "editable": true }, @@ -232,7 +232,7 @@ }, { "cell_type": "markdown", - "id": "3ee94591", + "id": "6b532ca6", "metadata": { "editable": true }, @@ -244,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "8c3e6340", + "id": "7fd0bcd8", "metadata": { "editable": true }, @@ -257,7 +257,7 @@ }, { "cell_type": "markdown", - "id": "2af274c5", + "id": "60502389", "metadata": { "editable": true }, @@ -269,7 +269,7 @@ }, { "cell_type": "markdown", - "id": "bca66e74", + "id": "cbe32b34", "metadata": { "editable": true }, @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "d47e35c4", + "id": "12186d6e", "metadata": { "editable": true }, @@ -293,7 +293,7 @@ }, { "cell_type": "markdown", - "id": "12a8fdcc", + "id": "fcb8d56c", "metadata": { "editable": true }, @@ -303,7 +303,7 @@ }, { "cell_type": "markdown", - "id": "d87c7fa2", + "id": "adcc6c78", "metadata": { "editable": true }, @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "6f09acf5", + "id": "ed8a91a8", "metadata": { "editable": true }, @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "f9e989b5", + "id": "d8ee2f04", "metadata": { "editable": true }, @@ -338,7 +338,7 @@ }, { "cell_type": "markdown", - "id": "3441658a", + "id": "8a5282be", "metadata": { "editable": true }, @@ -351,7 +351,7 @@ }, { "cell_type": "markdown", - "id": "18fcc762", + "id": "6864075d", "metadata": { "editable": true }, @@ -363,7 +363,7 @@ }, { "cell_type": "markdown", - "id": "a7b42213", + "id": "b8d30ca8", "metadata": { "editable": true }, @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "66e274ef", + "id": "a2d08a2d", "metadata": { "editable": true }, @@ -402,7 +402,7 @@ }, { "cell_type": "markdown", - "id": "b5efc0f5", + "id": "66f55738", "metadata": { "editable": true }, @@ -414,7 +414,7 @@ }, { "cell_type": "markdown", - "id": "d0ce1ec0", + "id": "325b47fb", "metadata": { "editable": true }, @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "dbcafecc", + "id": "7d7fbf12", "metadata": { "editable": true }, @@ -440,7 +440,7 @@ }, { "cell_type": "markdown", - "id": "32bc3f50", + "id": "91066108", "metadata": { "editable": true }, @@ -452,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "bafb22b8", + "id": "3c5f028b", "metadata": { "editable": true }, @@ -466,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "16db7223", + "id": "01aea9cd", "metadata": { "editable": true }, @@ -478,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "f63bd476", + "id": "def8f1fc", "metadata": { "editable": true }, @@ -488,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "fff030d2", + "id": "034d7e72", "metadata": { "editable": true }, @@ -500,7 +500,7 @@ }, { "cell_type": "markdown", - "id": "a6f4588b", + "id": "a18e2f52", "metadata": { "editable": true }, @@ -510,7 +510,7 @@ }, { "cell_type": "markdown", - "id": "6fe7ccdb", + "id": "27e7a61a", "metadata": { "editable": true }, @@ -522,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "20e37e16", + "id": "e8188a27", "metadata": { "editable": true }, @@ -534,7 +534,7 @@ }, { "cell_type": "markdown", - "id": "f9d8b077", + "id": "e7df215c", "metadata": { "editable": true }, @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "c2e764bf", + "id": "aef531b4", "metadata": { "editable": true }, @@ -556,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "30a68fe7", + "id": "b73a9613", "metadata": { "editable": true }, @@ -568,7 +568,7 @@ }, { "cell_type": "markdown", - "id": "6287f31a", + "id": "93b93705", "metadata": { "editable": true }, @@ -578,7 +578,7 @@ }, { "cell_type": "markdown", - "id": "b30507d7", + "id": "2acb6d5c", "metadata": { "editable": true }, @@ -592,7 +592,7 @@ }, { "cell_type": "markdown", - "id": "12a1c276", + "id": "42451c9c", "metadata": { "editable": true }, @@ -611,7 +611,7 @@ }, { "cell_type": "markdown", - "id": "fd7ee8f9", + "id": "875c0666", "metadata": { "editable": true }, @@ -627,7 +627,7 @@ }, { "cell_type": "markdown", - "id": "e2c00b64", + "id": "1815e5a6", "metadata": { "editable": true }, @@ -639,7 +639,7 @@ }, { "cell_type": "markdown", - "id": "e03f902b", + "id": "e9e0cf87", "metadata": { "editable": true }, @@ -651,7 +651,7 @@ }, { "cell_type": "markdown", - "id": "c1149e56", + "id": "7d4147d6", "metadata": { "editable": true }, @@ -663,7 +663,7 @@ }, { "cell_type": "markdown", - "id": "1d474d46", + "id": "aa538edd", "metadata": { "editable": true }, @@ -673,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "bafad3c4", + "id": "515cabfa", "metadata": { "editable": true }, @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "921253dc", + "id": "98ad7d18", "metadata": { "editable": true }, @@ -696,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "310e8e10", + "id": "1e1f5365", "metadata": { "editable": true }, @@ -708,7 +708,7 @@ }, { "cell_type": "markdown", - "id": "f5528998", + "id": "d8b4a59e", "metadata": { "editable": true }, @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "5fff5bd3", + "id": "110cb7ee", "metadata": { "editable": true }, @@ -734,7 +734,7 @@ }, { "cell_type": "markdown", - "id": "c5a39a05", + "id": "b769d022", "metadata": { "editable": true }, @@ -746,7 +746,7 @@ }, { "cell_type": "markdown", - "id": "1008e3d0", + "id": "514d49d9", "metadata": { "editable": true }, @@ -756,7 +756,7 @@ }, { "cell_type": "markdown", - "id": "a54a869c", + "id": "ce2e8468", "metadata": { "editable": true }, @@ -768,7 +768,7 @@ }, { "cell_type": "markdown", - "id": "4fea03b8", + "id": "faae74f3", "metadata": { "editable": true }, @@ -778,7 +778,7 @@ }, { "cell_type": "markdown", - "id": "0b7c2587", + "id": "596f292e", "metadata": { "editable": true }, @@ -790,7 +790,7 @@ }, { "cell_type": "markdown", - "id": "8eae6ec0", + "id": "50946c25", "metadata": { "editable": true }, @@ -807,7 +807,7 @@ }, { "cell_type": "markdown", - "id": "34f4ab4d", + "id": "c9803b27", "metadata": { "editable": true }, @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "a6f81ad5", + "id": "61dd7d63", "metadata": { "editable": true }, @@ -829,7 +829,7 @@ }, { "cell_type": "markdown", - "id": "063c8bfa", + "id": "f8371778", "metadata": { "editable": true }, @@ -841,7 +841,7 @@ }, { "cell_type": "markdown", - "id": "1cc9c467", + "id": "c9eaf0be", "metadata": { "editable": true }, @@ -851,7 +851,7 @@ }, { "cell_type": "markdown", - "id": "effdb65f", + "id": "b829b68d", "metadata": { "editable": true }, @@ -863,7 +863,7 @@ }, { "cell_type": "markdown", - "id": "c149944a", + "id": "ce089ead", "metadata": { "editable": true }, @@ -876,7 +876,7 @@ }, { "cell_type": "markdown", - "id": "3fae1f1a", + "id": "ad85648b", "metadata": { "editable": true }, @@ -888,7 +888,7 @@ }, { "cell_type": "markdown", - "id": "c59b582c", + "id": "b2823efd", "metadata": { "editable": true }, @@ -900,7 +900,7 @@ }, { "cell_type": "markdown", - "id": "db0802a3", + "id": "61eb6a55", "metadata": { "editable": true }, @@ -910,7 +910,7 @@ }, { "cell_type": "markdown", - "id": "82c41220", + "id": "e3b50194", "metadata": { "editable": true }, @@ -924,7 +924,7 @@ }, { "cell_type": "markdown", - "id": "6f984557", + "id": "10fe759e", "metadata": { "editable": true }, @@ -934,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "b03a2b62", + "id": "fa95693e", "metadata": { "editable": true }, @@ -946,7 +946,7 @@ }, { "cell_type": "markdown", - "id": "4eb578a6", + "id": "33dc27e7", "metadata": { "editable": true }, @@ -956,7 +956,7 @@ }, { "cell_type": "markdown", - "id": "7475b626", + "id": "8a97e646", "metadata": { "editable": true }, @@ -968,7 +968,7 @@ }, { "cell_type": "markdown", - "id": "438355a6", + "id": "9833d58a", "metadata": { "editable": true }, @@ -978,7 +978,7 @@ }, { "cell_type": "markdown", - "id": "cce886e7", + "id": "56748148", "metadata": { "editable": true }, @@ -990,7 +990,7 @@ }, { "cell_type": "markdown", - "id": "bff1f582", + "id": "5aafb764", "metadata": { "editable": true }, @@ -1004,7 +1004,7 @@ }, { "cell_type": "markdown", - "id": "e81b7b2f", + "id": "82cd5c73", "metadata": { "editable": true }, @@ -1016,7 +1016,7 @@ }, { "cell_type": "markdown", - "id": "2ce5b2a1", + "id": "35dc5cd9", "metadata": { "editable": true }, @@ -1027,7 +1027,7 @@ }, { "cell_type": "markdown", - "id": "85810a29", + "id": "2770972c", "metadata": { "editable": true }, @@ -1039,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "f50f6bde", + "id": "7d7c478c", "metadata": { "editable": true }, @@ -1051,7 +1051,7 @@ }, { "cell_type": "markdown", - "id": "344b46aa", + "id": "394e17a9", "metadata": { "editable": true }, @@ -1063,7 +1063,7 @@ }, { "cell_type": "markdown", - "id": "4ef104fc", + "id": "6d085f58", "metadata": { "editable": true }, @@ -1073,7 +1073,7 @@ }, { "cell_type": "markdown", - "id": "bc2b9dfe", + "id": "5512ef2d", "metadata": { "editable": true }, @@ -1085,7 +1085,7 @@ }, { "cell_type": "markdown", - "id": "6d9c4562", + "id": "022d5960", "metadata": { "editable": true }, @@ -1095,7 +1095,7 @@ }, { "cell_type": "markdown", - "id": "05417321", + "id": "3e32ab66", "metadata": { "editable": true }, @@ -1107,7 +1107,7 @@ }, { "cell_type": "markdown", - "id": "4e183182", + "id": "1e4819da", "metadata": { "editable": true }, @@ -1118,7 +1118,7 @@ }, { "cell_type": "markdown", - "id": "ce9b970a", + "id": "d747b0ba", "metadata": { "editable": true }, @@ -1130,7 +1130,7 @@ }, { "cell_type": "markdown", - "id": "ccbfa94c", + "id": "a6a4d86a", "metadata": { "editable": true }, @@ -1144,7 +1144,7 @@ }, { "cell_type": "markdown", - "id": "c38eaa68", + "id": "28f83c49", "metadata": { "editable": true }, @@ -1156,7 +1156,7 @@ }, { "cell_type": "markdown", - "id": "c087fe29", + "id": "582c0490", "metadata": { "editable": true }, @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "2774377e", + "id": "d91f1fba", "metadata": { "editable": true }, @@ -1178,7 +1178,7 @@ }, { "cell_type": "markdown", - "id": "0f16c79b", + "id": "d8489e22", "metadata": { "editable": true }, @@ -1195,7 +1195,7 @@ }, { "cell_type": "markdown", - "id": "ac4fb275", + "id": "15cf6b01", "metadata": { "editable": true }, @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "76c55fcb", + "id": "7ae516c8", "metadata": { "editable": true }, @@ -1219,7 +1219,7 @@ }, { "cell_type": "markdown", - "id": "e8f1e35f", + "id": "a4475f57", "metadata": { "editable": true }, @@ -1231,7 +1231,7 @@ }, { "cell_type": "markdown", - "id": "ddd24079", + "id": "db157b5b", "metadata": { "editable": true }, @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "4bb8b010", + "id": "62e61a7b", "metadata": { "editable": true }, @@ -1253,7 +1253,7 @@ }, { "cell_type": "markdown", - "id": "d7dd9be1", + "id": "cc75cb92", "metadata": { "editable": true }, @@ -1263,7 +1263,7 @@ }, { "cell_type": "markdown", - "id": "dc1925ab", + "id": "b28d44ef", "metadata": { "editable": true }, @@ -1275,7 +1275,7 @@ }, { "cell_type": "markdown", - "id": "07708b9e", + "id": "5d9a5a73", "metadata": { "editable": true }, @@ -1285,7 +1285,7 @@ }, { "cell_type": "markdown", - "id": "156ce798", + "id": "7014e7a3", "metadata": { "editable": true }, @@ -1297,7 +1297,7 @@ }, { "cell_type": "markdown", - "id": "c8ad4670", + "id": "974813b7", "metadata": { "editable": true }, @@ -1307,7 +1307,7 @@ }, { "cell_type": "markdown", - "id": "ee247125", + "id": "d1691443", "metadata": { "editable": true }, @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "8a9c208a", + "id": "630ea9df", "metadata": { "editable": true }, @@ -1329,7 +1329,7 @@ }, { "cell_type": "markdown", - "id": "0e5241d1", + "id": "873d9437", "metadata": { "editable": true }, @@ -1349,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "9d3cac30", + "id": "7479ef27", "metadata": { "editable": true }, @@ -1361,7 +1361,7 @@ }, { "cell_type": "markdown", - "id": "7ca91202", + "id": "9d24f832", "metadata": { "editable": true }, @@ -1371,7 +1371,7 @@ }, { "cell_type": "markdown", - "id": "d0f85f66", + "id": "f54f1925", "metadata": { "editable": true }, @@ -1383,7 +1383,7 @@ }, { "cell_type": "markdown", - "id": "129a83e7", + "id": "1ab5f910", "metadata": { "editable": true }, @@ -1399,7 +1399,7 @@ }, { "cell_type": "markdown", - "id": "7db3fc50", + "id": "a8cfa315", "metadata": { "editable": true }, @@ -1411,7 +1411,7 @@ }, { "cell_type": "markdown", - "id": "9469f55a", + "id": "928c273c", "metadata": { "editable": true }, @@ -1423,7 +1423,7 @@ }, { "cell_type": "markdown", - "id": "cfbf2c29", + "id": "89c3a2a8", "metadata": { "editable": true }, @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "2ebd374a", + "id": "3444a4f1", "metadata": { "editable": true }, @@ -1445,7 +1445,7 @@ }, { "cell_type": "markdown", - "id": "a993b0d9", + "id": "1c648831", "metadata": { "editable": true }, @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "2b1e0823", + "id": "9a53ac4a", "metadata": { "editable": true }, @@ -1469,7 +1469,7 @@ }, { "cell_type": "markdown", - "id": "037a3bfa", + "id": "d93cd756", "metadata": { "editable": true }, @@ -1479,7 +1479,7 @@ }, { "cell_type": "markdown", - "id": "72972735", + "id": "41e268fc", "metadata": { "editable": true }, @@ -1491,7 +1491,7 @@ }, { "cell_type": "markdown", - "id": "febd69ec", + "id": "fcbaafba", "metadata": { "editable": true }, @@ -1501,7 +1501,7 @@ }, { "cell_type": "markdown", - "id": "c87b8890", + "id": "3128acc6", "metadata": { "editable": true }, @@ -1513,7 +1513,7 @@ }, { "cell_type": "markdown", - "id": "7237810a", + "id": "68eee6ce", "metadata": { "editable": true }, @@ -1523,7 +1523,7 @@ }, { "cell_type": "markdown", - "id": "918f18b5", + "id": "f68dee1a", "metadata": { "editable": true }, @@ -1535,7 +1535,7 @@ }, { "cell_type": "markdown", - "id": "f198fdb6", + "id": "6aadb5c2", "metadata": { "editable": true }, @@ -1546,7 +1546,7 @@ }, { "cell_type": "markdown", - "id": "125ea351", + "id": "a8c28ca0", "metadata": { "editable": true }, @@ -1558,7 +1558,7 @@ }, { "cell_type": "markdown", - "id": "1dffe0b7", + "id": "b5a714d4", "metadata": { "editable": true }, @@ -1570,7 +1570,7 @@ }, { "cell_type": "markdown", - "id": "1f12ddb7", + "id": "54ab9331", "metadata": { "editable": true }, @@ -1580,7 +1580,7 @@ }, { "cell_type": "markdown", - "id": "05cab070", + "id": "5b16a8d8", "metadata": { "editable": true }, @@ -1592,7 +1592,7 @@ }, { "cell_type": "markdown", - "id": "08d86471", + "id": "7e663d27", "metadata": { "editable": true }, @@ -1618,7 +1618,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "230c91f3", + "id": "c37b6b57", "metadata": { "collapsed": false, "editable": true @@ -1675,7 +1675,7 @@ }, { "cell_type": "markdown", - "id": "0c5b991e", + "id": "c02cfa09", "metadata": { "editable": true }, @@ -1687,7 +1687,7 @@ }, { "cell_type": "markdown", - "id": "700d72c5", + "id": "23ce8aa9", "metadata": { "editable": true }, @@ -1699,7 +1699,7 @@ }, { "cell_type": "markdown", - "id": "cfca1e23", + "id": "56d1e78f", "metadata": { "editable": true }, @@ -1709,7 +1709,7 @@ }, { "cell_type": "markdown", - "id": "2116ccc7", + "id": "bcaa6bfb", "metadata": { "editable": true }, @@ -1721,7 +1721,7 @@ }, { "cell_type": "markdown", - "id": "2079f218", + "id": "c6889b36", "metadata": { "editable": true }, @@ -1731,7 +1731,7 @@ }, { "cell_type": "markdown", - "id": "66b5fe61", + "id": "bbb242b8", "metadata": { "editable": true }, @@ -1743,7 +1743,7 @@ }, { "cell_type": "markdown", - "id": "3d073811", + "id": "54c69269", "metadata": { "editable": true }, @@ -1754,7 +1754,7 @@ }, { "cell_type": "markdown", - "id": "3342c153", + "id": "198117f5", "metadata": { "editable": true }, @@ -1766,7 +1766,7 @@ }, { "cell_type": "markdown", - "id": "cd72c270", + "id": "bb6a092d", "metadata": { "editable": true }, @@ -1776,7 +1776,7 @@ }, { "cell_type": "markdown", - "id": "5ef9f9ad", + "id": "7c7963db", "metadata": { "editable": true }, @@ -1788,7 +1788,7 @@ }, { "cell_type": "markdown", - "id": "5543c696", + "id": "0bb4529c", "metadata": { "editable": true }, @@ -1806,7 +1806,7 @@ }, { "cell_type": "markdown", - "id": "e132c137", + "id": "d0860da4", "metadata": { "editable": true }, @@ -1817,7 +1817,7 @@ }, { "cell_type": "markdown", - "id": "0c862436", + "id": "02cbb83e", "metadata": { "editable": true }, @@ -1829,7 +1829,7 @@ }, { "cell_type": "markdown", - "id": "8f382055", + "id": "21ab8e0a", "metadata": { "editable": true }, @@ -1839,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "8089b8c2", + "id": "0ccde505", "metadata": { "editable": true }, @@ -1856,7 +1856,7 @@ }, { "cell_type": "markdown", - "id": "f3511a48", + "id": "d325c73a", "metadata": { "editable": true }, @@ -1870,7 +1870,7 @@ }, { "cell_type": "markdown", - "id": "1cb8e709", + "id": "14abe1e0", "metadata": { "editable": true }, @@ -1885,7 +1885,7 @@ }, { "cell_type": "markdown", - "id": "c9ff556c", + "id": "4aa701ba", "metadata": { "editable": true }, @@ -1897,7 +1897,7 @@ }, { "cell_type": "markdown", - "id": "399564cd", + "id": "de58ef40", "metadata": { "editable": true }, @@ -1925,7 +1925,7 @@ }, { "cell_type": "markdown", - "id": "6a9fa597", + "id": "aa95c803", "metadata": { "editable": true }, @@ -1937,7 +1937,7 @@ }, { "cell_type": "markdown", - "id": "36ddb4e1", + "id": "e44cbcef", "metadata": { "editable": true }, @@ -1952,7 +1952,7 @@ }, { "cell_type": "markdown", - "id": "3df4d0f5", + "id": "7d15bfb0", "metadata": { "editable": true }, @@ -1963,7 +1963,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "fc3071f8", + "id": "4516246b", "metadata": { "collapsed": false, "editable": true @@ -2162,7 +2162,7 @@ }, { "cell_type": "markdown", - "id": "9257c492", + "id": "70711864", "metadata": { "editable": true }, @@ -2174,7 +2174,7 @@ }, { "cell_type": "markdown", - "id": "d8570913", + "id": "8effd67e", "metadata": { "editable": true }, @@ -2189,7 +2189,7 @@ }, { "cell_type": "markdown", - "id": "7f505d2d", + "id": "d02e2818", "metadata": { "editable": true }, @@ -2206,7 +2206,7 @@ }, { "cell_type": "markdown", - "id": "40432a05", + "id": "76c05a44", "metadata": { "editable": true }, @@ -2227,7 +2227,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "57f02399", + "id": "80889c0c", "metadata": { "collapsed": false, "editable": true @@ -2240,7 +2240,7 @@ }, { "cell_type": "markdown", - "id": "0a70c791", + "id": "cf7d92a4", "metadata": { "editable": true }, @@ -2250,7 +2250,7 @@ }, { "cell_type": "markdown", - "id": "4e3f9d0a", + "id": "2ef9611b", "metadata": { "editable": true }, @@ -2262,7 +2262,7 @@ }, { "cell_type": "markdown", - "id": "bc3598a1", + "id": "3a00902a", "metadata": { "editable": true }, @@ -2277,7 +2277,7 @@ }, { "cell_type": "markdown", - "id": "91dc0901", + "id": "1cb22c82", "metadata": { "editable": true }, @@ -2287,7 +2287,7 @@ }, { "cell_type": "markdown", - "id": "afc664b3", + "id": "ba290e45", "metadata": { "editable": true }, @@ -2306,7 +2306,7 @@ }, { "cell_type": "markdown", - "id": "9a25ee47", + "id": "9b9e61f8", "metadata": { "editable": true }, @@ -2316,7 +2316,7 @@ }, { "cell_type": "markdown", - "id": "60ab9094", + "id": "bf906cdd", "metadata": { "editable": true }, @@ -2328,7 +2328,7 @@ }, { "cell_type": "markdown", - "id": "f66da708", + "id": "de6f4dda", "metadata": { "editable": true }, @@ -2338,7 +2338,7 @@ }, { "cell_type": "markdown", - "id": "0f0bbe9d", + "id": "5a0cf8d8", "metadata": { "editable": true }, @@ -2350,7 +2350,7 @@ }, { "cell_type": "markdown", - "id": "913728fb", + "id": "e4d78d0d", "metadata": { "editable": true }, @@ -2360,7 +2360,7 @@ }, { "cell_type": "markdown", - "id": "8818d39f", + "id": "dcc18a57", "metadata": { "editable": true }, @@ -2372,7 +2372,7 @@ }, { "cell_type": "markdown", - "id": "c2872e7e", + "id": "17ef78ff", "metadata": { "editable": true }, @@ -2383,7 +2383,7 @@ }, { "cell_type": "markdown", - "id": "48a6fa42", + "id": "2a994afc", "metadata": { "editable": true }, @@ -2395,7 +2395,7 @@ }, { "cell_type": "markdown", - "id": "aae264b5", + "id": "b59ed115", "metadata": { "editable": true }, @@ -2407,7 +2407,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "333380fc", + "id": "2b8129eb", "metadata": { "collapsed": false, "editable": true @@ -2431,7 +2431,7 @@ }, { "cell_type": "markdown", - "id": "47774759", + "id": "d41b30f0", "metadata": { "editable": true }, @@ -2443,7 +2443,7 @@ }, { "cell_type": "markdown", - "id": "504a67ba", + "id": "71194f96", "metadata": { "editable": true }, @@ -2460,7 +2460,7 @@ }, { "cell_type": "markdown", - "id": "f1684b8d", + "id": "13cb5251", "metadata": { "editable": true }, @@ -2474,7 +2474,7 @@ }, { "cell_type": "markdown", - "id": "da0a9e67", + "id": "2b6d7d6e", "metadata": { "editable": true }, @@ -2484,7 +2484,7 @@ }, { "cell_type": "markdown", - "id": "7c019123", + "id": "8931b59e", "metadata": { "editable": true }, @@ -2499,7 +2499,77 @@ }, { "cell_type": "markdown", - "id": "5a4d20e0", + "id": "03727abd", + "metadata": { + "editable": true + }, + "source": [ + "## What is the link between Artificial Intelligence and Machine Learning and some general Remarks\n", + "\n", + "Artificial intelligence is built upon integrated machine learning\n", + "algorithms as discussed in this course, which in turn are fundamentally rooted in optimization and\n", + "statistical learning.\n", + "\n", + "Can we have Artificial Intelligence without Machine Learning? See [this post for inspiration](https://www.linkedin.com/pulse/what-artificial-intelligence-without-machine-learning-claudia-pohlink)." + ] + }, + { + "cell_type": "markdown", + "id": "f72ea138", + "metadata": { + "editable": true + }, + "source": [ + "## Going back to the beginning of the semester\n", + "\n", + "Traditionally the field of machine learning has had its main focus on\n", + "predictions and correlations. These concepts outline in some sense\n", + "the difference between machine learning and what is normally called\n", + "Bayesian statistics or Bayesian inference.\n", + "\n", + "In machine learning and prediction based tasks, we are often\n", + "interested in developing algorithms that are capable of learning\n", + "patterns from given data in an automated fashion, and then using these\n", + "learned patterns to make predictions or assessments of newly given\n", + "data. In many cases, our primary concern is the quality of the\n", + "predictions or assessments, and we are less concerned with the\n", + "underlying patterns that were learned in order to make these\n", + "predictions. This leads to what normally has been labeled as a\n", + "frequentist approach." + ] + }, + { + "cell_type": "markdown", + "id": "e57c523f", + "metadata": { + "editable": true + }, + "source": [ + "## Not so sharp distinctions\n", + "\n", + "You should keep in mind that the division between a traditional\n", + "frequentist approach with focus on predictions and correlations only\n", + "and a Bayesian approach with an emphasis on estimations and\n", + "causations, is not that sharp. Machine learning can be frequentist\n", + "with ensemble methods (EMB) as examples and Bayesian with Gaussian\n", + "Processes as examples.\n", + "\n", + "If one views ML from a statistical learning\n", + "perspective, one is then equally interested in estimating errors as\n", + "one is in finding correlations and making predictions. It is important\n", + "to keep in mind that the frequentist and Bayesian approaches differ\n", + "mainly in their interpretations of probability. In the frequentist\n", + "world, we can only assign probabilities to repeated random\n", + "phenomena. From the observations of these phenomena, we can infer the\n", + "probability of occurrence of a specific event. In Bayesian\n", + "statistics, we assign probabilities to specific events and the\n", + "probability represents the measure of belief/confidence for that\n", + "event. The belief can be updated in the light of new evidence." + ] + }, + { + "cell_type": "markdown", + "id": "7490d694", "metadata": { "editable": true }, @@ -2515,7 +2585,7 @@ }, { "cell_type": "markdown", - "id": "bcc74df9", + "id": "4f7bb20b", "metadata": { "editable": true }, @@ -2540,7 +2610,7 @@ }, { "cell_type": "markdown", - "id": "2b8801ca", + "id": "b83826b7", "metadata": { "editable": true }, @@ -2587,7 +2657,7 @@ }, { "cell_type": "markdown", - "id": "39636325", + "id": "134d371b", "metadata": { "editable": true }, @@ -2623,7 +2693,7 @@ }, { "cell_type": "markdown", - "id": "2988c391", + "id": "e131c86a", "metadata": { "editable": true }, @@ -2646,7 +2716,7 @@ }, { "cell_type": "markdown", - "id": "a6c56a87", + "id": "2f7b0955", "metadata": { "editable": true }, @@ -2669,7 +2739,7 @@ }, { "cell_type": "markdown", - "id": "a6c25620", + "id": "49fa4b7b", "metadata": { "editable": true }, @@ -2689,7 +2759,7 @@ }, { "cell_type": "markdown", - "id": "fd2b7113", + "id": "7d5c5a1c", "metadata": { "editable": true }, @@ -2705,7 +2775,7 @@ }, { "cell_type": "markdown", - "id": "7c9a5de1", + "id": "d556fdb2", "metadata": { "editable": true }, @@ -2733,7 +2803,7 @@ }, { "cell_type": "markdown", - "id": "36595e5e", + "id": "a4ba04de", "metadata": { "editable": true }, @@ -2755,7 +2825,7 @@ }, { "cell_type": "markdown", - "id": "2df6352a", + "id": "82576d56", "metadata": { "editable": true }, @@ -2780,7 +2850,7 @@ }, { "cell_type": "markdown", - "id": "656a0b89", + "id": "3957ee87", "metadata": { "editable": true }, @@ -2798,7 +2868,7 @@ }, { "cell_type": "markdown", - "id": "8e8e192b", + "id": "8f43da22", "metadata": { "editable": true }, @@ -2826,7 +2896,7 @@ }, { "cell_type": "markdown", - "id": "fc14460e", + "id": "e08a1bc0", "metadata": { "editable": true }, @@ -2840,7 +2910,7 @@ }, { "cell_type": "markdown", - "id": "629ee91b", + "id": "cf1b3d0d", "metadata": { "editable": true }, @@ -2866,7 +2936,7 @@ }, { "cell_type": "markdown", - "id": "cf4db5aa", + "id": "8dca4061", "metadata": { "editable": true }, @@ -2895,7 +2965,7 @@ }, { "cell_type": "markdown", - "id": "1d5fab05", + "id": "b9f9edee", "metadata": { "editable": true }, @@ -2912,7 +2982,7 @@ }, { "cell_type": "markdown", - "id": "6b991d53", + "id": "08640fb4", "metadata": { "editable": true }, @@ -2934,7 +3004,7 @@ }, { "cell_type": "markdown", - "id": "a5932cee", + "id": "9557e3f5", "metadata": { "editable": true }, @@ -2952,7 +3022,7 @@ }, { "cell_type": "markdown", - "id": "0ad0d650", + "id": "8a30742f", "metadata": { "editable": true }, @@ -2975,7 +3045,7 @@ }, { "cell_type": "markdown", - "id": "4339da25", + "id": "0cea4abf", "metadata": { "editable": true }, @@ -2994,7 +3064,7 @@ }, { "cell_type": "markdown", - "id": "cb7a6993", + "id": "f13fb9f6", "metadata": { "editable": true }, @@ -3010,7 +3080,7 @@ }, { "cell_type": "markdown", - "id": "3f30c28c", + "id": "81d6be62", "metadata": { "editable": true }, @@ -3025,7 +3095,7 @@ }, { "cell_type": "markdown", - "id": "6b876456", + "id": "c9da5a3f", "metadata": { "editable": true }, @@ -3049,7 +3119,7 @@ }, { "cell_type": "markdown", - "id": "47d3be36", + "id": "a1ea8499", "metadata": { "editable": true }, @@ -3061,7 +3131,7 @@ }, { "cell_type": "markdown", - "id": "29765f69", + "id": "ff723dbd", "metadata": { "editable": true }, @@ -3079,7 +3149,7 @@ }, { "cell_type": "markdown", - "id": "9ba56beb", + "id": "84fe806f", "metadata": { "editable": true }, @@ -3089,7 +3159,7 @@ }, { "cell_type": "markdown", - "id": "cd2d0682", + "id": "29932151", "metadata": { "editable": true }, @@ -3107,7 +3177,7 @@ }, { "cell_type": "markdown", - "id": "dc1f40cd", + "id": "5e6fb888", "metadata": { "editable": true }, @@ -3117,7 +3187,7 @@ }, { "cell_type": "markdown", - "id": "9c839416", + "id": "1f48a948", "metadata": { "editable": true }, @@ -3136,7 +3206,7 @@ }, { "cell_type": "markdown", - "id": "278181fd", + "id": "62302009", "metadata": { "editable": true }, @@ -3148,7 +3218,7 @@ }, { "cell_type": "markdown", - "id": "ffff0a4e", + "id": "03ad1fab", "metadata": { "editable": true }, @@ -3162,7 +3232,7 @@ }, { "cell_type": "markdown", - "id": "0e543d62", + "id": "36861f13", "metadata": { "editable": true }, @@ -3177,7 +3247,7 @@ }, { "cell_type": "markdown", - "id": "e0a7f3bb", + "id": "3ed52b2d", "metadata": { "editable": true }, @@ -3195,7 +3265,7 @@ }, { "cell_type": "markdown", - "id": "c651acd8", + "id": "c28fbd39", "metadata": { "editable": true }, @@ -3209,7 +3279,7 @@ }, { "cell_type": "markdown", - "id": "9d921b6b", + "id": "5ab7e1fd", "metadata": { "editable": true }, @@ -3227,7 +3297,7 @@ }, { "cell_type": "markdown", - "id": "9b948560", + "id": "deaa82f8", "metadata": { "editable": true }, @@ -3251,7 +3321,7 @@ }, { "cell_type": "markdown", - "id": "753fbdad", + "id": "4a3b2c29", "metadata": { "editable": true }, @@ -3289,7 +3359,7 @@ }, { "cell_type": "markdown", - "id": "327bb176", + "id": "2b0f5287", "metadata": { "editable": true }, @@ -3312,7 +3382,7 @@ }, { "cell_type": "markdown", - "id": "6915d98d", + "id": "03fdfcaf", "metadata": { "editable": true }, @@ -3348,7 +3418,7 @@ }, { "cell_type": "markdown", - "id": "6159229d", + "id": "230473cf", "metadata": { "editable": true }, @@ -3369,7 +3439,7 @@ }, { "cell_type": "markdown", - "id": "70f3d5e8", + "id": "846d275b", "metadata": { "editable": true }, @@ -3391,7 +3461,7 @@ }, { "cell_type": "markdown", - "id": "aab7e5b4", + "id": "ed7f7eed", "metadata": { "editable": true }, @@ -3411,7 +3481,7 @@ }, { "cell_type": "markdown", - "id": "785fd98b", + "id": "94f27695", "metadata": { "editable": true }, @@ -3426,7 +3496,7 @@ }, { "cell_type": "markdown", - "id": "cf1b999b", + "id": "2ae2b5d9", "metadata": { "editable": true }, @@ -3444,7 +3514,7 @@ }, { "cell_type": "markdown", - "id": "04581208", + "id": "c8170bd4", "metadata": { "editable": true }, @@ -3474,7 +3544,7 @@ }, { "cell_type": "markdown", - "id": "d1580b89", + "id": "770c9f3b", "metadata": { "editable": true }, @@ -3503,7 +3573,7 @@ }, { "cell_type": "markdown", - "id": "66f9e349", + "id": "24ab7dc9", "metadata": { "editable": true }, @@ -3534,7 +3604,7 @@ }, { "cell_type": "markdown", - "id": "dcf8e4fe", + "id": "daff58eb", "metadata": { "editable": true }, @@ -3557,7 +3627,7 @@ }, { "cell_type": "markdown", - "id": "d1d76bc1", + "id": "49afc03f", "metadata": { "editable": true }, @@ -3575,7 +3645,7 @@ }, { "cell_type": "markdown", - "id": "46aaee9d", + "id": "6279873b", "metadata": { "editable": true }, @@ -3598,7 +3668,7 @@ }, { "cell_type": "markdown", - "id": "73c0f7ff", + "id": "10760689", "metadata": { "editable": true }, @@ -3619,7 +3689,7 @@ }, { "cell_type": "markdown", - "id": "d102eb89", + "id": "5245b378", "metadata": { "editable": true }, @@ -3635,7 +3705,7 @@ }, { "cell_type": "markdown", - "id": "f82ffc60", + "id": "7bce2b05", "metadata": { "editable": true }, diff --git a/doc/src/week47/week47.do.txt b/doc/src/week47/week47.do.txt index ae93805c5..6ded92c4b 100644 --- a/doc/src/week47/week47.do.txt +++ b/doc/src/week47/week47.do.txt @@ -1165,6 +1165,56 @@ FIGURE: [figures/exam1.jpeg, width=500 frac=0.6] +!split +===== What is the link between Artificial Intelligence and Machine Learning and some general Remarks ===== + +Artificial intelligence is built upon integrated machine learning +algorithms as discussed in this course, which in turn are fundamentally rooted in optimization and +statistical learning. + +Can we have Artificial Intelligence without Machine Learning? See "this post for inspiration":"https://www.linkedin.com/pulse/what-artificial-intelligence-without-machine-learning-claudia-pohlink". + +!split +===== Going back to the beginning of the semester ===== + +Traditionally the field of machine learning has had its main focus on +predictions and correlations. These concepts outline in some sense +the difference between machine learning and what is normally called +Bayesian statistics or Bayesian inference. + +In machine learning and prediction based tasks, we are often +interested in developing algorithms that are capable of learning +patterns from given data in an automated fashion, and then using these +learned patterns to make predictions or assessments of newly given +data. In many cases, our primary concern is the quality of the +predictions or assessments, and we are less concerned with the +underlying patterns that were learned in order to make these +predictions. This leads to what normally has been labeled as a +frequentist approach. + +!split +===== Not so sharp distinctions ===== + +You should keep in mind that the division between a traditional +frequentist approach with focus on predictions and correlations only +and a Bayesian approach with an emphasis on estimations and +causations, is not that sharp. Machine learning can be frequentist +with ensemble methods (EMB) as examples and Bayesian with Gaussian +Processes as examples. + +If one views ML from a statistical learning +perspective, one is then equally interested in estimating errors as +one is in finding correlations and making predictions. It is important +to keep in mind that the frequentist and Bayesian approaches differ +mainly in their interpretations of probability. In the frequentist +world, we can only assign probabilities to repeated random +phenomena. From the observations of these phenomena, we can infer the +probability of occurrence of a specific event. In Bayesian +statistics, we assign probabilities to specific events and the +probability represents the measure of belief/confidence for that +event. The belief can be updated in the light of new evidence. + + !split ===== Topics we have covered this year =====