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Teaching plan fall 2018, miss reading assignments and project deadlines

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mhjensen
2018-08-09 22:52:50 +02:00
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doc/web/course.do.txt
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@@ -213,7 +213,7 @@ Acronyms for textbooks and references to chapter
|----------------------------------------------------------------------------------------------------------------------------|
| Week and days | Topics to be covered | Projects and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and Regressions analysis | | | |
| Week 34| Introduction and regression analysis | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | | | |
|----------------------------------------------------------------------------------------------------------------------------|
@@ -221,25 +221,25 @@ Acronyms for textbooks and references to chapter
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Statistics, Monte Carlo and Randow walks | | | |
| Week 38 | Optimization methods | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Statistics, Monte Carlo and Randow walks | | | |
| Week 39 | Statistics, Bayesian statistics | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Statistics, Monte Carlo and Randow walks | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Neural Networks | | | |
| Week 41 | Statistics, Monte Carlo, Gibbs and Metropolis sampling | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural Networks | | | |
| Week 42 | Neural networks | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Neural Nteworks | | | |
| Week 43 | Neural networks | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | | | | |
| Week 44 | Neural networks | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | | | | |
| Week 45 | Support Vector Machines | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | | | | |
| Week 46 | Decision trees | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | | | | |
| Week 47 | Unsupervised learning, Boltzmann machines | | | |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | | | | |
| Week 48 | Unsupervised learning and summary of course | | | |
|----------------------------------------------------------------------------------------------------------------------------|
doc/web/course.html
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@@ -697,26 +697,33 @@ All the above topics will be supported by examples, hands-on exercises and proje
<h2 id="___sec24">Teaching schedule Fall 2018 </h2>
<p>
Acronyms for textbooks and references to chapter
<ul>
<li> Hastie</li>
<li> Geron</li>
</ul>
<table border="1">
<thead>
<tr><th align="center">Week and days</th> <th align="center"> Topics to be covered </th> <th align="center">Projects and deadlines</th> <th align="center">Reading assignments</th> <th align="center">Lab activities</th> </tr>
<tr><th align="center">Week and days</th> <th align="center"> Topics to be covered </th> <th align="center">Projects and deadlines</th> <th align="center">Reading assignments</th> <th align="center">Lab activities</th> </tr>
</thead>
<tbody>
<tr><td align="center"> Week 34 </td> <td align="center"> Introduction and Regressions analysis </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 35 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 36 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 37 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 38 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 39 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 40 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 41 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 42 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 43 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 44 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 45 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 46 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 47 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 48 </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 34 </td> <td align="center"> Introduction and regression analysis </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 35 </td> <td align="center"> Regression analysis </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 36 </td> <td align="center"> Regression analysis and nearest neighbors </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 37 </td> <td align="center"> Classification and logistic regression </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 38 </td> <td align="center"> Optimization methods </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 39 </td> <td align="center"> Statistics, Bayesian statistics </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 40 </td> <td align="center"> Statistics, Monte Carlo and Randow walks </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 41 </td> <td align="center"> Statistics, Monte Carlo, Gibbs and Metropolis sampling </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 42 </td> <td align="center"> Neural networks </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 43 </td> <td align="center"> Neural networks </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 44 </td> <td align="center"> Neural networks </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 45 </td> <td align="center"> Support Vector Machines </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 46 </td> <td align="center"> Decision trees </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 47 </td> <td align="center"> Unsupervised learning, Boltzmann machines </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
<tr><td align="center"> Week 48 </td> <td align="center"> Unsupervised learning and summary of course </td> <td align="center"> </td> <td align="center"> </td> <td align="center"> </td> </tr>
</tbody>
</table>