Updates

prep/class.md

@@ -2,12 +2,9 @@
 
 As a preparation for the Supervised Learning convocation, make yourself acquainted with the material below, and subsequently complete any tasks assigned to you in canvas.
 
-2. Read the sections in the Data Science Book on [Classification](https://kaell.se/dsbook/classification), VaderPlas on [Model Validation](https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html) and [Support vector machines](https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html).
-3. Read Noble, [What is a support vector machine](https://www.nature.com/articles/nbt1206-1565).
-4. Investigate the jupyter notebook on [Supervised Learning](../nb/supervised/). There are associated study questions [here](../nb/supervised/questions.md).
-5. Read previous years [questions and answers](../qa/supervisedlearning) on the material  
+2. Read the sections in the Data Science Book on [Classification](https://kaell.se/dsbook/classification), and [Validation] and [Support vector machines](https://www.kaell.se/dsbook/supervised/xval.html).
+5. Perform the assignments in Canvas
 
 ## Additional Material;
 
-1. StatQuest on [SVMs](https://www.youtube.com/watch?v=efR1C6CvhmE)
-2. SVMs in [2 minutes](https://www.youtube.com/watch?v=_YPScrckx28) 
+1. Jake VanderPlas on [Model Validation](https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html)

prep/hypothesistesting.md

@@ -2,10 +2,8 @@
 
 As a preparation for the Hypothesis Testing convocation, make yourself acquainted with the material below, and subsequently complete any tasks assigned to you in canvas.
 
-1. Watch the [Video Lecture on Hypothesis Testing](https://youtu.be/qeANWTCVbG0), and its [slides](slides/HypothesisTesting.pdf)
-2. Read the chapter 8.3 and 9.1-9.5 in Downey on [hypothesis testing](http://greenteapress.com/thinkstats2/html/thinkstats2010.html#sec89). The associated code is available [here](https://github.com/AllenDowney/ThinkStats2/blob/master/code/chap09ex.ipynb).
-    * The book gives a different method for how to derive *p* values from samples than most other books. They derive *p* values from simulations, rather than to look them up from formulas or tables.  If you prefer a traditional explanation, try e.g.  [wikipedia's entry on *t* tests](https://en.wikipedia.org/wiki/Student%27s_t-test).
-    * Unfortunately, Downey does not give a distinct definition of a *p* value. The correct definition is ["the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct."](https://en.wikipedia.org/wiki/P-value), however, in the glosary 9.13 an other definitions is given. Try to convince yourself that you understand why the glosaries definitions is problematic. See for instance [this wikipedia entry](https://en.wikipedia.org/wiki/Misuse_of_p-values).
+
+2. Read the sections in the Data Science Book on [Hypothesis Testing](https://www.kaell.se/dsbook/statistics/significance.html).
 3. Investigate the jupyter notebook on [Differential expression anlaysis of the TCGA breast cancer set](../nb/testing/). If you want some study questions we provide some [here](../nb/testing/questions.md)  
 4. Read previous years [questions and answers](../qa/testing) on the material. 
 

prep/readme.md

@@ -4,14 +4,11 @@ Here we keep the material to prepare for the questions and answers lectures
 
 - [Regression](regression.md)
 - [Classification, Valdiation](class.md)
-- [Hypothesis Testing](hypothesistesting.md)  
-- [Multiple Testing](multipletesting.md)
-- [Linear Models](linearmodels.md)  
-
-
-- [Supervised Learning](supervised.md)  
+- [SVMs, Neural Networks](svm_nn.md)
 - [Clustering](clustering.md)  
 
+- [Hypothesis Testing](hypothesistesting.md)  
+- [Multiple Testing](multipletesting.md)
 
 - [Principal Component Analysis](pca.md)
 - [Variational Auto Encoder](vae.md)  

prep/svm_nn.md

@@ -0,0 +1,14 @@
+## Preparations for lecture on SVMs an Neural Networks
+
+As a preparation for the Supervised Learning convocation, make yourself acquainted with the material below, and subsequently complete any tasks assigned to you in canvas.
+
+2. Read the sections in the Data Science Book on [SVMs](https://www.kaell.se/dsbook/supervised/svm.html), and [Neural Networks](https://www.kaell.se/dsbook/supervised/mlp.html).
+4. Investigate the jupyter notebook on [Classification](https://www.kaell.se/dsbook/supervised/supervisedML.html). 
+5. Perform the assignments in Canvas
+
+## Additional Material;
+
+1. Jake VanderPlas on [Support vector machines](https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html)
+1. Noble, [What is a support vector machine](https://www.nature.com/articles/nbt1206-1565).
+1. StatQuest on [SVMs](https://www.youtube.com/watch?v=efR1C6CvhmE)
+2. SVMs in [2 minutes](https://www.youtube.com/watch?v=_YPScrckx28)