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Morten Hjorth-Jensen
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@@ -28,55 +28,72 @@ For the reading assignments we use the following abbreviations:
- Video of Lecture August 26, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage
- Lecture Friday: Basics of Linear Regression
- Video of Lecture August 27, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage
- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
- Reading recommendations:
- Refresh linear algebra, GBC chapters 1 and 2.
- CMB sections 1.1 and 3.1.
- HTF chapters 2 and 3.
- See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
### Week 35 August 30-September 3
- Lab Wednesday: Work on exercises 1-3 for week 35
- Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition
- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage".
- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage.
- Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition
- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage"
- Reading recommendations: See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1
- CMB sections 1.1 and 3.1
### Week 36 September 6-10
- Lab Wednesday: Exercises 1 and 2 from week 36
- Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression
- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage".
- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage
- Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project.
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage"
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage
- Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3
- Reading recommendations:
- Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1
- Hastie et al chapter 3
### Week 37 September 13-17
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
- Lecture Friday: More on Resampling methods and summary of linear regression
- Reading recommendations:
- Recommended Reading:
- Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)
- Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop's on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5.
- Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)
- Goodfellow et al discuss some of these topics in sections 5.2-5.5.
### Week 38 September 20-24
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
- Lecture Friday: Logistic Regression and gradient optimization
- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Reading recommendations:
- See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### Week 39 September 27- October 1
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Gradient Optimization methods
- Lecture Friday: Deep Learning and Neural Networks
- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
- Reading recommendations:
- See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
### Week 40 October 4-8
- Lab Wednesday:
- Lab Wednesday: Wrap up project 1 and start project 2
- Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
- Lecture Friday: Deep Learning and TensorFlow and Keras
- Reading recommendations:
- Chapter
### Week 41 October 11-15
- Lab Wednesday:
- Lecture Thursday: Deep learning and Neural Networks
@@ -88,43 +105,43 @@ For the reading assignments we use the following abbreviations:
- Lecture Thursday: Convolutional Neural Networks and classification problems
- Lecture Friday: Convolutional Neural Networks and classification problems
- Reading recommendations:
- Chapter
### Week 43 October 25-29
- Lab Wednesday:
- Lecture Thursday: Recurrent Neural Networks
- Lecture Friday: Recurrent Neural Networks and time series
- Reading recommendations:
- Chapter
### Week 44 November 1-5
- Lab Wednesday:
- Lecture Thursday: Decision trees, classification and regression
- Lecture Friday: Decision trees, basic algorithms
- Reading recommendations:
- Chapter
### Week 45 November 8-12
- Lab Wednesday:
- Lecture Thursday: Ensemble methods, bagging and random forests
- Lecture Friday: Boosting and gradient boosting
- Reading recommendations:
- Chapter
### Week 46 November 15-19
- Lab Wednesday:
- Lecture Thursday:
- Lecture Friday: Unsupervised Learning, k-means
- Reading recommendations:
- Chapter
### Week 47 November 22-26
- Lab Wednesday:
- Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)
- Lecture Friday: Unsupervised Learning and PCA and Clustering
- Reading recommendations:
- Chapter
### Week 48 November 29- December 2
- Lab Wednesday:
- Lecture Thursday: Unsupervised Learning and Clustering
- Lecture Friday: Summary of course
- Reading recommendations:
- Chapter
doc/LectureNotes/_build/html/schedule.html
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@@ -412,7 +412,14 @@
<li><p>Video of Lecture August 26, 2021 at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage</a></p></li>
<li><p>Lecture Friday: Basics of Linear Regression</p></li>
<li><p>Video of Lecture August 27, 2021 at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage</a></p></li>
<li><p>Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 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>Reading recommendations:</p>
<ul>
<li><p>Refresh linear algebra, GBC chapters 1 and 2.</p></li>
<li><p>CMB sections 1.1 and 3.1.</p></li>
<li><p>HTF chapters 2 and 3.</p></li>
<li><p>See lecture notes for week 34 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>
</ul>
</li>
</ul>
</div>
<div class="section" id="week-35-august-30-september-3">
@@ -420,10 +427,16 @@
<ul class="simple">
<li><p>Lab Wednesday: Work on exercises 1-3 for week 35</p></li>
<li><p>Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition</p></li>
<li><p>Video of lecture Thursday at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage</a>”.</p></li>
<li><p>Video of lecture Thursday at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage</a>.</p></li>
<li><p>Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition</p></li>
<li><p>Video of lecture Friday at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage</a>”</p></li>
<li><p>Reading recommendations: See lecture notes for week 35 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>. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1</p></li>
<li><p>Video of lecture Friday at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage</a></p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>See lecture notes for week 35 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>HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1</p></li>
<li><p>CMB sections 1.1 and 3.1</p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="week-36-september-6-10">
@@ -431,24 +444,30 @@
<ul class="simple">
<li><p>Lab Wednesday: Exercises 1 and 2 from week 36</p></li>
<li><p>Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression</p></li>
<li><p>Video of Lecture <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage</a>”.</p></li>
<li><p>Video of Lecture <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage</a></p></li>
<li><p>Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project.</p></li>
<li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage</a>”</p></li>
<li><p>Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3</p></li>
<li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage</a></p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Lectures on Regression for week 36 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>Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1</p></li>
<li><p>Hastie et al chapter 3</p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="week-37-september-13-17">
<h3>Week 37 September 13-17<a class="headerlink" href="#week-37-september-13-17" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li><p>Lab Wednesday:</p></li>
<li><p>Lab Wednesday: Work on Project 1</p></li>
<li><p>Lecture Thursday: Resampling methods, cross-validation and Bootstrap</p></li>
<li><p>Lecture Friday: More on Resampling methods and summary of linear regression</p></li>
<li><p>Reading recommendations:</p></li>
<li><p>Recommended Reading:</p>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Lectures on Resampling methods for week 37 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>Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)</p></li>
<li><p>Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop’s on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5.</p></li>
<li><p>Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)</p></li>
<li><p>Goodfellow et al discuss some of these topics in sections 5.2-5.5.</p></li>
</ul>
</li>
</ul>
@@ -456,11 +475,12 @@
<div class="section" id="week-38-september-20-24">
<h3>Week 38 September 20-24<a class="headerlink" href="#week-38-september-20-24" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li><p>Lab Wednesday:</p></li>
<li><p>Lab Wednesday: Work on Project 1</p></li>
<li><p>Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories</p></li>
<li><p>Lecture Friday: Logistic Regression and gradient optimization</p></li>
<li><p>Reading recommendations: See lecture notes for week 38 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><p>Reading recommendations:</p>
<ul>
<li><p>See lecture notes for week 38 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>Chapter</p></li>
</ul>
</li>
@@ -469,12 +489,12 @@
<div class="section" id="week-39-september-27-october-1">
<h3>Week 39 September 27- October 1<a class="headerlink" href="#week-39-september-27-october-1" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li><p>Lab Wednesday:</p></li>
<li><p>Lab Wednesday: Work on Project 1</p></li>
<li><p>Lecture Thursday: Gradient Optimization methods</p></li>
<li><p>Lecture Friday: Deep Learning and Neural Networks</p></li>
<li><p>Reading recommendations: See lecture notes for week 39 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><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
<li><p>See lecture notes for week 39 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>
</ul>
</li>
</ul>
@@ -482,14 +502,10 @@
<div class="section" id="week-40-october-4-8">
<h3>Week 40 October 4-8<a class="headerlink" href="#week-40-october-4-8" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li><p>Lab Wednesday:</p></li>
<li><p>Lab Wednesday: Wrap up project 1 and start project 2</p></li>
<li><p>Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification</p></li>
<li><p>Lecture Friday: Deep Learning and TensorFlow and Keras</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-41-october-11-15">
@@ -511,11 +527,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Convolutional Neural Networks and classification problems</p></li>
<li><p>Lecture Friday: Convolutional Neural Networks and classification problems</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-43-october-25-29">
@@ -524,11 +536,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Recurrent Neural Networks</p></li>
<li><p>Lecture Friday: Recurrent Neural Networks and time series</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-44-november-1-5">
@@ -537,11 +545,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Decision trees, classification and regression</p></li>
<li><p>Lecture Friday: Decision trees, basic algorithms</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-45-november-8-12">
@@ -550,11 +554,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Ensemble methods, bagging and random forests</p></li>
<li><p>Lecture Friday: Boosting and gradient boosting</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-46-november-15-19">
@@ -563,11 +563,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday:</p></li>
<li><p>Lecture Friday: Unsupervised Learning, k-means</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-47-november-22-26">
@@ -576,11 +572,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)</p></li>
<li><p>Lecture Friday: Unsupervised Learning and PCA and Clustering</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
<div class="section" id="week-48-november-29-december-2">
@@ -589,11 +581,7 @@
<li><p>Lab Wednesday:</p></li>
<li><p>Lecture Thursday: Unsupervised Learning and Clustering</p></li>
<li><p>Lecture Friday: Summary of course</p></li>
<li><p>Reading recommendations:</p>
<ul>
<li><p>Chapter</p></li>
</ul>
</li>
<li><p>Reading recommendations:</p></li>
</ul>
</div>
</div>
doc/LectureNotes/_build/html/searchindex.js
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doc/LectureNotes/schedule.md
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@@ -28,55 +28,72 @@ For the reading assignments we use the following abbreviations:
- Video of Lecture August 26, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage
- Lecture Friday: Basics of Linear Regression
- Video of Lecture August 27, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage
- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
- Reading recommendations:
- Refresh linear algebra, GBC chapters 1 and 2.
- CMB sections 1.1 and 3.1.
- HTF chapters 2 and 3.
- See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
### Week 35 August 30-September 3
- Lab Wednesday: Work on exercises 1-3 for week 35
- Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition
- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage".
- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage.
- Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition
- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage"
- Reading recommendations: See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1
- CMB sections 1.1 and 3.1
### Week 36 September 6-10
- Lab Wednesday: Exercises 1 and 2 from week 36
- Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression
- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage".
- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage
- Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project.
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage"
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage
- Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3
- Reading recommendations:
- Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1
- Hastie et al chapter 3
### Week 37 September 13-17
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
- Lecture Friday: More on Resampling methods and summary of linear regression
- Reading recommendations:
- Recommended Reading:
- Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)
- Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop's on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5.
- Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)
- Goodfellow et al discuss some of these topics in sections 5.2-5.5.
### Week 38 September 20-24
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
- Lecture Friday: Logistic Regression and gradient optimization
- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Reading recommendations:
- See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### Week 39 September 27- October 1
- Lab Wednesday:
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Gradient Optimization methods
- Lecture Friday: Deep Learning and Neural Networks
- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
- Reading recommendations:
- See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
### Week 40 October 4-8
- Lab Wednesday:
- Lab Wednesday: Wrap up project 1 and start project 2
- Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
- Lecture Friday: Deep Learning and TensorFlow and Keras
- Reading recommendations:
- Chapter
### Week 41 October 11-15
- Lab Wednesday:
- Lecture Thursday: Deep learning and Neural Networks
@@ -88,43 +105,43 @@ For the reading assignments we use the following abbreviations:
- Lecture Thursday: Convolutional Neural Networks and classification problems
- Lecture Friday: Convolutional Neural Networks and classification problems
- Reading recommendations:
- Chapter
### Week 43 October 25-29
- Lab Wednesday:
- Lecture Thursday: Recurrent Neural Networks
- Lecture Friday: Recurrent Neural Networks and time series
- Reading recommendations:
- Chapter
### Week 44 November 1-5
- Lab Wednesday:
- Lecture Thursday: Decision trees, classification and regression
- Lecture Friday: Decision trees, basic algorithms
- Reading recommendations:
- Chapter
### Week 45 November 8-12
- Lab Wednesday:
- Lecture Thursday: Ensemble methods, bagging and random forests
- Lecture Friday: Boosting and gradient boosting
- Reading recommendations:
- Chapter
### Week 46 November 15-19
- Lab Wednesday:
- Lecture Thursday:
- Lecture Friday: Unsupervised Learning, k-means
- Reading recommendations:
- Chapter
### Week 47 November 22-26
- Lab Wednesday:
- Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)
- Lecture Friday: Unsupervised Learning and PCA and Clustering
- Reading recommendations:
- Chapter
### Week 48 November 29- December 2
- Lab Wednesday:
- Lecture Thursday: Unsupervised Learning and Clustering
- Lecture Friday: Summary of course
- Reading recommendations:
- Chapter