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added some text to week 47

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Morten Hjorth-Jensen
2021-11-25 05:37:29 +01:00
parent ce33548cb5
commit 4bdb70a008
7 changed files with 604 additions and 277 deletions
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doc/pub/week47/html/week47-bs.html
+63 -47
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@@ -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({
<!-- navigation toc: --> <li><a href="._week47-bs028.html#back-to-the-more-realistic-cases" style="font-size: 80%;">Back to the more realistic cases</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs029.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs030.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs031.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs032.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs033.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs034.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs035.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs036.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs037.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs038.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs039.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs040.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs041.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs042.html#resampling" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs043.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs044.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs045.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs046.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs047.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs048.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs049.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs050.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs051.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs052.html#the-structure-of-the-rbm-network" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs053.html#the-network" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs054.html#goals" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs055.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs056.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs057.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs058.html#more-about-rbms" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs059.html#autoencoders-overarching-view" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs060.html#bayesian-machine-learning" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs061.html#reinforcement-learning" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs062.html#transfer-learning" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs063.html#adversarial-learning" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs064.html#dual-learning" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs065.html#distributed-machine-learning" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs066.html#meta-learning" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs067.html#the-challenges-facing-machine-learning" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs068.html#explainable-machine-learning" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs069.html#quantum-machine-learning" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs070.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs071.html#quantum-reinforcement-learning" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs072.html#quantum-deep-learning" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs073.html#social-machine-learning" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs074.html#the-last-words" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs075.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs031.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;">What is the link between Artificial Intelligence and Machine Learning and some general Remarks</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs032.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;">Going back to the beginning of the semester</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs033.html#not-so-sharp-distinctions" style="font-size: 80%;">Not so sharp distinctions</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs034.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs035.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs036.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs037.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs038.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs039.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs040.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs041.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs042.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs043.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs044.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs045.html#resampling" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs046.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs047.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs049.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs050.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs051.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs052.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs053.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs054.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs055.html#the-structure-of-the-rbm-network" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs056.html#the-network" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs057.html#goals" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs058.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs059.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs060.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs061.html#more-about-rbms" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs062.html#autoencoders-overarching-view" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs063.html#bayesian-machine-learning" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs064.html#reinforcement-learning" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs065.html#transfer-learning" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs066.html#adversarial-learning" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs067.html#dual-learning" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs068.html#distributed-machine-learning" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs069.html#meta-learning" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs070.html#the-challenges-facing-machine-learning" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs071.html#explainable-machine-learning" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs072.html#quantum-machine-learning" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs073.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs074.html#quantum-reinforcement-learning" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs075.html#quantum-deep-learning" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs076.html#social-machine-learning" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs077.html#the-last-words" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week47-bs078.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
</ul>
</li>
@@ -355,7 +371,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 20, 2021</h4>
<h4>Nov 25, 2021</h4>
</center> <!-- date -->
<br>
@@ -380,7 +396,7 @@ MathJax.Hub.Config({
<li><a href="._week47-bs008.html">9</a></li>
<li><a href="._week47-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._week47-bs075.html">76</a></li>
<li><a href="._week47-bs078.html">79</a></li>
<li><a href="._week47-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week47/html/week47-reveal.html
+58 -1
View File
@@ -184,7 +184,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 20, 2021</h4>
<h4>Nov 25, 2021</h4>
</center> <!-- date -->
<br>
@@ -1601,6 +1601,63 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb
<br/><br/>
</section>
<section>
<h2 id="what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks">What is the link between Artificial Intelligence and Machine Learning and some general Remarks </h2>
<p>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.
</p>
<p>Can we have Artificial Intelligence without Machine Learning? See <a href="https://www.linkedin.com/pulse/what-artificial-intelligence-without-machine-learning-claudia-pohlink" target="_blank">this post for inspiration</a>.</p>
</section>
<section>
<h2 id="going-back-to-the-beginning-of-the-semester">Going back to the beginning of the semester </h2>
<p>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.
</p>
<p>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.
</p>
</section>
<section>
<h2 id="not-so-sharp-distinctions">Not so sharp distinctions </h2>
<p>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.
</p>
<p>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.
</p>
</section>
<section>
<h2 id="topics-we-have-covered-this-year">Topics we have covered this year </h2>
doc/pub/week47/html/week47-solarized.html
+68 -1
View File
@@ -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({
</center>
<br>
<center>
<h4>Nov 20, 2021</h4>
<h4>Nov 25, 2021</h4>
</center> <!-- date -->
<br>
@@ -1528,6 +1541,60 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb
</center>
<br/><br/>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks">What is the link between Artificial Intelligence and Machine Learning and some general Remarks </h2>
<p>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.
</p>
<p>Can we have Artificial Intelligence without Machine Learning? See <a href="https://www.linkedin.com/pulse/what-artificial-intelligence-without-machine-learning-claudia-pohlink" target="_blank">this post for inspiration</a>.</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="going-back-to-the-beginning-of-the-semester">Going back to the beginning of the semester </h2>
<p>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.
</p>
<p>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.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="not-so-sharp-distinctions">Not so sharp distinctions </h2>
<p>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.
</p>
<p>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.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="topics-we-have-covered-this-year">Topics we have covered this year </h2>
doc/pub/week47/html/week47.html
+68 -1
View File
@@ -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({
</center>
<br>
<center>
<h4>Nov 20, 2021</h4>
<h4>Nov 25, 2021</h4>
</center> <!-- date -->
<br>
@@ -1605,6 +1618,60 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb
</center>
<br/><br/>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks">What is the link between Artificial Intelligence and Machine Learning and some general Remarks </h2>
<p>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.
</p>
<p>Can we have Artificial Intelligence without Machine Learning? See <a href="https://www.linkedin.com/pulse/what-artificial-intelligence-without-machine-learning-claudia-pohlink" target="_blank">this post for inspiration</a>.</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="going-back-to-the-beginning-of-the-semester">Going back to the beginning of the semester </h2>
<p>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.
</p>
<p>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.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="not-so-sharp-distinctions">Not so sharp distinctions </h2>
<p>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.
</p>
<p>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.
</p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="topics-we-have-covered-this-year">Topics we have covered this year </h2>
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!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 =====