updated course layout after meeting

course_layout.md

@@ -93,30 +93,6 @@ TBD: prepare coding session
   - learn regressor for movie scores.
 
 
-## Part 4: accuracy, F1, ROC, ...
-
-Intention: accuracy is usefull but has pitfalls
-
-- how to measure accuracy ?
-
-  - (TDB: skip ?) regression accuracy
-  - 
-  - classifier accuracy:
-    - confusion matrix
-    - accurarcy
-    - pitfalls for unbalanced data sets~
-        e.g. diagnose HIV
-    - precision / recall
-    - ROC ?
-
-### Coding session
-
-- evaluate accuracy of linear beer classifier from latest section
-
-- determine precision / recall
-
-- fool them: give them other dataset where classifier fails.
-
 ## Part 3: underfitting/overfitting
 
 needs: simple accuracy measure.
@@ -148,20 +124,58 @@ classifiers / regressors have parameters / degrees of freedom.
 - ? run crossvalidation on movie regression problem
 
 
-## Part 6: pipelines / parameter tuning with scikit-learn
+## Part 4: accuracy, F1, ROC, ...
+
+Intention: accuracy is usefull but has pitfalls
+
+- how to measure accuracy ?
+
+  - (TDB: skip ?) regression accuracy
+  - 
+  - classifier accuracy:
+    - confusion matrix
+    - accurarcy
+    - pitfalls for unbalanced data sets~
+        e.g. diagnose HIV
+    - precision / recall
+    - ROC ?
+    
+- exercise: do cross val with other metrics
+
+### Coding session
+
+- evaluate accuracy of linear beer classifier from latest section
+
+- determine precision / recall
+
+- fool them: give them other dataset where classifier fails.
+
+
+# Day 2
+
+
+## Part 5: pipelines / parameter tuning with scikit-learn
 
 - Scicit learn api:  recall what we have seen up to now.
 - pipelines, preprocessing (scaler, PCA)
 - cross validatioon
 - parameter tuning: grid search / random search.
 
+
+### Coding session
+
+- build SVM and Random forest crossval pipelines for previous examples
+- use PCA in pipeline for (+) to improve performance
+- find optimal SVM parameters
+- find optimal pca components number
+
 ### Coding par
 
 Planning: stop here, make time estimates.
 
-# DAY 2
 
-### Part 6:
+
+## Part 6: classifiers overview
 
 Intention: quick walk throught throug reliable classifiers, give some background
 idea if suitable, let them play withs some incl. modification of parameters.
@@ -175,28 +189,27 @@ diagram.
 - Random forests
 - Gradient Tree Boosting
 
-### Part 7: Start with neural networks. .5 day
+show decision surfaces of these classifiers on 2d examples.
 
+### Coding session
+
+- apply SVM, Random Forests, Gradient boosting to previous examples
+- apply clustering to previous examples
+- MNIST example
+
+
+## Part 7: Start with neural networks. .5 day
 
 
 
 
 
-### Coding session
 
-- apply SVM, Random Forests, Gradient boosting to previous examples
-- apply clustering to previous examples
-- MNIST example
 
 
-### Coding session
 
-- build SVM and Random forest crossval pipelines for previous examples
-- use PCA in pipeline for (+) to improve performance
-- find optimal SVM parameters
-- find optimal pca components number
 
-## Part 7: Best practices
+## Part 8: Best practices
 
 - visualize features: pairwise scatter, tSNE
 - PCA to undertand data
@@ -204,7 +217,7 @@ diagram.
 - start with baseline classifier / regressor
 - augment data to introduce variance
 
-## Part 8: neural networks
+## Part 9: neural networks
 
 - overview, history
 - perceptron
@@ -216,3 +229,8 @@ diagram.
 ### Coding Session
 
 - keras reuse network and play with it.
+
+
+
+
+