book update
This commit is contained in:
Morten Hjorth-Jensen
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* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
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* _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.
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## Teaching Assistants Fall semester 2022
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* Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no
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* Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no
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## Teaching Assistants Fall semester 2023
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* Karl Henrik Fredly, k.h.fredly@fys.uio.no
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* Daniel Haas Becattini Lima, d.h.b.lima@fys.uio.no
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* Adam Jakobsen, adam.jakobsen@fys.uio.no
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* Fahimeh Najafi, fahimeh.najafi@fys.uio.no
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* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
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* Joao Guilherme Carvalho Inacio, joaogca@fys.uio.no
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* Sigurd Sørlie Rustad, s.s.rustad@fys.uio.no
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* Stian Dysthe Bilek stian.bilek@fys.uio.no
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* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
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* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
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## Practicalities
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1. The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.
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2. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.
|
||||
|
||||
3. On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. These lectures are recorded.
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4. Three projects which are graded and count 1/3 each of the final grade;
|
||||
5. A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.
|
||||
6. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
|
||||
7. The course is offered as a so-called _cloned_ course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;
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8. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
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9. Weekly email with summary of activities will be mailed to all participants;
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## Grading
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@@ -31,6 +44,13 @@ The final number of points is based on the average of all projects (including ev
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* 0-39 points: F-failed
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### In summary
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| Activity | Percentage of total score |
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|------|-----|
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| First project, _due October 9_ | 33.3% (1/3) |
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||||
| Second project, _due November 6_ | 33.3% (1.3) |
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| Third project, _due December 11_ | 33.3% (1/3) |
|
||||
| Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)| 20% |
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||||
|
||||
|
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The first weekly exercise set is scheduled for week 35.
|
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@@ -6,7 +6,8 @@ The lecture notes are collected as a jupyter-book at https://compphysics.github.
|
||||
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
|
||||
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text.
|
||||
- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
|
||||
The weekly plans will include reading suggestions from these two textbooks.
|
||||
|
||||
The weekly plans will include reading suggestions from the above textbooks.
|
||||
_Additional textbooks_:
|
||||
- Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
|
||||
- Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
|
||||
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File diff suppressed because one or more lines are too long
@@ -316,14 +316,26 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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</li>
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2022">
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Teaching Assistants Fall semester 2022
|
||||
<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2023">
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Teaching Assistants Fall semester 2023
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h2 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#practicalities">
|
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Practicalities
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h2 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#grading">
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Grading
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</a>
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<ul class="nav section-nav flex-column">
|
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<li class="toc-h3 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#in-summary">
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In summary
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</a>
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</li>
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</ul>
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</li>
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</ul>
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@@ -351,14 +363,26 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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</li>
|
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<li class="toc-h2 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2022">
|
||||
Teaching Assistants Fall semester 2022
|
||||
<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2023">
|
||||
Teaching Assistants Fall semester 2023
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h2 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#practicalities">
|
||||
Practicalities
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h2 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#grading">
|
||||
Grading
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||||
</a>
|
||||
<ul class="nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#in-summary">
|
||||
In summary
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
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</ul>
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@@ -381,18 +405,31 @@ const thebe_selector_output = ".output, .cell_output"
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<li><p><em>Office hours</em>: <em>Anytime</em>! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.</p></li>
|
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</ul>
|
||||
</div>
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<div class="section" id="teaching-assistants-fall-semester-2022">
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<h2>Teaching Assistants Fall semester 2022<a class="headerlink" href="#teaching-assistants-fall-semester-2022" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="teaching-assistants-fall-semester-2023">
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<h2>Teaching Assistants Fall semester 2023<a class="headerlink" href="#teaching-assistants-fall-semester-2023" title="Permalink to this headline">¶</a></h2>
|
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<ul class="simple">
|
||||
<li><p>Bennosh Ashrafi, <a class="reference external" href="mailto:behnoosh.ashrafi%40fys.uio.no">behnoosh<span>.</span>ashrafi<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
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<li><p>Frida Marie Engøy Westbye, <a class="reference external" href="mailto:f.m.e.westby%40fys.uio.no">f<span>.</span>m<span>.</span>e<span>.</span>westby<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Karl Henrik Fredly, <a class="reference external" href="mailto:k.h.fredly%40fys.uio.no">k<span>.</span>h<span>.</span>fredly<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Daniel Haas Becattini Lima, <a class="reference external" href="mailto:d.h.b.lima%40fys.uio.no">d<span>.</span>h<span>.</span>b<span>.</span>lima<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Adam Jakobsen, <a class="reference external" href="mailto:adam.jakobsen%40fys.uio.no">adam<span>.</span>jakobsen<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Fahimeh Najafi, <a class="reference external" href="mailto:fahimeh.najafi%40fys.uio.no">fahimeh<span>.</span>najafi<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Ida Torkjellsdatter Storehaug, <a class="reference external" href="mailto:i.t.storehaug%40fys.uio.no">i<span>.</span>t<span>.</span>storehaug<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Joao Guilherme Carvalho Inacio, <a class="reference external" href="mailto:joaogca%40fys.uio.no">joaogca<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Sigurd Sørlie Rustad, <a class="reference external" href="mailto:s.s.rustad%40fys.uio.no">s<span>.</span>s<span>.</span>rustad<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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||||
<li><p>Stian Dysthe Bilek <a class="reference external" href="mailto:stian.bilek%40fys.uio.no">stian<span>.</span>bilek<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Øyvind Sigmundson Schøyen, <a class="reference external" href="mailto:oyvinssc%40student.matnat.uio.no">oyvinssc<span>@</span>student<span>.</span>matnat<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
<li><p>Mia-Katrin Ose Kvalsund, <a class="reference external" href="mailto:m.k.o.kvalsund%40fys.uio.no">m<span>.</span>k<span>.</span>o<span>.</span>kvalsund<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section" id="practicalities">
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||||
<h2>Practicalities<a class="headerlink" href="#practicalities" title="Permalink to this headline">¶</a></h2>
|
||||
<ol class="simple">
|
||||
<li><p>The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.</p></li>
|
||||
<li><p>There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.</p></li>
|
||||
<li><p>On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. These lectures are recorded.</p></li>
|
||||
<li><p>Three projects which are graded and count 1/3 each of the final grade;</p></li>
|
||||
<li><p>A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.</p></li>
|
||||
<li><p>The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;</p></li>
|
||||
<li><p>The course is offered as a so-called <em>cloned</em> course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;</p></li>
|
||||
<li><p>Videos of teaching material are available via the links at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>;</p></li>
|
||||
<li><p>Weekly email with summary of activities will be mailed to all participants;</p></li>
|
||||
</ol>
|
||||
</div>
|
||||
<div class="section" id="grading">
|
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<h2>Grading<a class="headerlink" href="#grading" title="Permalink to this headline">¶</a></h2>
|
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<p>Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.</p>
|
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@@ -405,6 +442,31 @@ const thebe_selector_output = ".output, .cell_output"
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<li><p>40-45 points: E</p></li>
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<li><p>0-39 points: F-failed</p></li>
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</ul>
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<div class="section" id="in-summary">
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<h3>In summary<a class="headerlink" href="#in-summary" title="Permalink to this headline">¶</a></h3>
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<table class="colwidths-auto table">
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<thead>
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<tr class="row-odd"><th class="head"><p>Activity</p></th>
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<th class="head"><p>Percentage of total score</p></th>
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</tr>
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</thead>
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<tbody>
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<tr class="row-even"><td><p>First project, <em>due October 9</em></p></td>
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<td><p>33.3% (1/3)</p></td>
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</tr>
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<tr class="row-odd"><td><p>Second project, <em>due November 6</em></p></td>
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<td><p>33.3% (1.3)</p></td>
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</tr>
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<tr class="row-even"><td><p>Third project, <em>due December 11</em></p></td>
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<td><p>33.3% (1/3)</p></td>
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</tr>
|
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<tr class="row-odd"><td><p>Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)</p></td>
|
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<td><p>20%</p></td>
|
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</tr>
|
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</tbody>
|
||||
</table>
|
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<p>The first weekly exercise set is scheduled for week 35.</p>
|
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</div>
|
||||
</div>
|
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</div>
|
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|
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|
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@@ -366,9 +366,11 @@ The lecture notes are collected as a jupyter-book at <a class="reference externa
|
||||
<ul class="simple">
|
||||
<li><p>Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387310732">https://www.springer.com/gp/book/9780387310732</a>. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at <a class="reference external" href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf">https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf</a></p></li>
|
||||
<li><p>Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a class="reference external" href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a>. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text.</p></li>
|
||||
<li><p>Kevin Murphy, Probabilistic Machine Learning, an Introduction, <a class="reference external" href="https://probml.github.io/pml-book/book1.html">https://probml.github.io/pml-book/book1.html</a>
|
||||
The weekly plans will include reading suggestions from these two textbooks.
|
||||
<em>Additional textbooks</em>:</p></li>
|
||||
<li><p>Kevin Murphy, Probabilistic Machine Learning, an Introduction, <a class="reference external" href="https://probml.github.io/pml-book/book1.html">https://probml.github.io/pml-book/book1.html</a></p></li>
|
||||
</ul>
|
||||
<p>The weekly plans will include reading suggestions from the above textbooks.
|
||||
<em>Additional textbooks</em>:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">https://www.springer.com/gp/book/9780387848570</a>. This is a well-known text and serves as additional literature.</p></li>
|
||||
<li><p>Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O’Reilly, <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</p></li>
|
||||
</ul>
|
||||
|
||||
@@ -8,14 +8,27 @@
|
||||
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
|
||||
* _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.
|
||||
|
||||
## Teaching Assistants Fall semester 2022
|
||||
* Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no
|
||||
* Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no
|
||||
## Teaching Assistants Fall semester 2023
|
||||
* Karl Henrik Fredly, k.h.fredly@fys.uio.no
|
||||
* Daniel Haas Becattini Lima, d.h.b.lima@fys.uio.no
|
||||
* Adam Jakobsen, adam.jakobsen@fys.uio.no
|
||||
* Fahimeh Najafi, fahimeh.najafi@fys.uio.no
|
||||
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
|
||||
* Joao Guilherme Carvalho Inacio, joaogca@fys.uio.no
|
||||
* Sigurd Sørlie Rustad, s.s.rustad@fys.uio.no
|
||||
* Stian Dysthe Bilek stian.bilek@fys.uio.no
|
||||
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
|
||||
* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
|
||||
|
||||
|
||||
## Practicalities
|
||||
|
||||
1. The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.
|
||||
2. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.
|
||||
|
||||
3. On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. These lectures are recorded.
|
||||
4. Three projects which are graded and count 1/3 each of the final grade;
|
||||
5. A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.
|
||||
6. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
|
||||
7. The course is offered as a so-called _cloned_ course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;
|
||||
8. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
|
||||
9. Weekly email with summary of activities will be mailed to all participants;
|
||||
|
||||
|
||||
## Grading
|
||||
@@ -31,6 +44,13 @@ The final number of points is based on the average of all projects (including ev
|
||||
* 0-39 points: F-failed
|
||||
|
||||
|
||||
### In summary
|
||||
|
||||
| Activity | Percentage of total score |
|
||||
|------|-----|
|
||||
| First project, _due October 9_ | 33.3% (1/3) |
|
||||
| Second project, _due November 6_ | 33.3% (1.3) |
|
||||
| Third project, _due December 11_ | 33.3% (1/3) |
|
||||
| Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)| 20% |
|
||||
|
||||
|
||||
The first weekly exercise set is scheduled for week 35.
|
||||
@@ -6,7 +6,8 @@ The lecture notes are collected as a jupyter-book at https://compphysics.github.
|
||||
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
|
||||
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text.
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- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
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The weekly plans will include reading suggestions from these two textbooks.
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The weekly plans will include reading suggestions from the above textbooks.
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_Additional textbooks_:
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- Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
|
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- Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
|
||||
|
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Reference in New Issue
Block a user