Merge branch 'master' of sissource.ethz.ch:sispub/machinelearning-introduction-workshop

content.md

@@ -7,7 +7,7 @@
 # Course structure
 
 - Two days workshop, 1.5 days workshop + .5 day working on own data / prepared data.
-- Every part below includes a coding session using Jupter notebooks.
+- Every part below includes a coding session using Jupyter notebooks.
 - Coding sessions provide code frames which should be completed.
 - We provide solutions.
 
@@ -16,7 +16,7 @@
 
 ## Part 0: Preparation
 
-- Quick basics matplotlib, numpy, pandas?:
+- Quick basics matplotlib, numpy, pandas?
 
 
 ### Coding session
@@ -59,7 +59,7 @@
 
 ## Part 3: accuracy, F1, ROC, ...
 
-Intention: accuracy is usefull but has pitfalls
+Intention: accuracy is useful but has pitfalls
 
 - how to measure accuracy ?
 
@@ -83,9 +83,8 @@ classifiers / regressors have parameters / degrees of freedom.
 
 - overfitting:
 
-  - features have actual noise, or not enough information
-    not enough information: orchid example in 2d. elevate to 3d using another feature.
-  - polnome of degree 5 to fit points on a line + noise
+  - features have actual noise, or not enough information: orchid example in 2d. elevate to 3d using another feature.
+  - polynome of degree 5 to fit points on a line + noise
   - points in a circle: draw very exact boundary line
 
 - how to check underfitting / overfitting ?
@@ -97,14 +96,14 @@ classifiers / regressors have parameters / degrees of freedom.
 ### Coding session:
 
 - How to do cross validation with scikit-learn
-- run crossvalidation on classifier for beer data
+- run cross validation on classifier for beer data
 
 
 ## Part 5: pipelines / parameter tuning with scikit-learn
 
-- Scicit learn API incl summary  what we have seen up to now.
+- Scikit learn API incl. summary of what we have seen up to now.
 - pipelines, preprocessing (scaler, PCA)
-- cross validatioon
+- cross validation
 - Hyper parameter tuning: grid search / random search.
 
 ### Coding session
@@ -116,7 +115,7 @@ classifiers / regressors have parameters / degrees of freedom.
 
 ## Part 6: Overview classifiers
 
-- Neighrest neighbours
+- Nearest neighbours
 - SVMs
   - demo for RBF: different parameters influence on decision line
 - Random forests
@@ -125,7 +124,7 @@ classifiers / regressors have parameters / degrees of freedom.
 
 ### Coding session
 
-- Prepare examples for 2d classification problems incl visualization of different
+- Prepare examples for 2d classification problems incl. visualization of different
   decision surfaces.
 
 - Play with different classifiers on beer data
@@ -146,7 +145,7 @@ Introduce movie data set, learn SVR or other regressor on this data set.
 
 
 - Overview of the field
-- Introduction feed forward neural networks
+- Introduction to feed forward neural networks
 - Demo Keras
 
 ### Coding Session
@@ -156,7 +155,7 @@ Introduce movie data set, learn SVR or other regressor on this data set.
 
 ## Workshop
 
-- assist to setup own computer.
+- assist to setup the workshop material on own computer.
 - provide example problems if attendees don't bring own data.