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@@ -14,13 +14,13 @@ project 1, including bootstrap _and/or_ cross-validation as well as the
 computation of the mean-squared error and/or the $R2$ or the accuracy score (classification problems) functions can
 also be utilized in the present analysis. 
 
-_Important Note_: This project as well as project 3 have to be written as a scientific report. The instructions on how to do this and how we grade are available at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md". We will discuss this format during the various lab sessions. Please do spend some time to read our guidelines.
+_Important Note_: This project as well as projects 1 and 3 have to be written as a scientific report. The instructions on how to do this and how we grade are available at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md". We will discuss this format during the various lab sessions. Please do spend some time to read our guidelines.
 
 The data sets that we propose here are (the default sets)
 
 * Regression (fitting a continuous function). In this part you will need to bring back your results from project 1 and compare these with what you get from your Neural Network code to be developed here. The data sets could be
-  o Either the Franke function or the terrain data from project 1, or data sets your propose.
-* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called "Wisconsin Breat Cancer Data":"https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from "week 40":"https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week40.html". A longer explanation with links to the scientific literature can be found at the "Machine Learning repository of the University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29". Feel free to consult this site and the pertinent  literature.
+  o Either the Franke function or the terrain data from project 1, or data sets your propose. It could be a simpler function than the Franke function.
+* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called "Wisconsin Breat Cancer Data":"https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" data set of images representing various features of tumors. These are discussed intensively in the lecture notes, see for example the slides from "week 41":"https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41.html". A longer explanation with links to the scientific literature can be found at the "Machine Learning repository of the University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29". Feel free to consult this site and the pertinent  literature.
 You can find more information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)". 
 
 
@@ -40,8 +40,8 @@ In order to get started, we will now replace in our standard ordinary
 least squares (OLS) and Ridge regression codes (from project 1) the matrix inversion
 algorithm with our own SGD code. You can choose whether you want to
 add the momentum SGD optionality or other SGD variants such as RMSprop
-or ADAgrad or ADAM. The lecture notes from "week 40 contain more
-details":"https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40.html"
+or ADAgrad or ADAM. The lecture notes from "week 39 contain more
+details":"https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html"
 
 Perform an analysis of the results for OLS and Ridge regression as
 function of the chosen learning rates, the number of mini-batches and