diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_1.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_1.m
deleted file mode 100644
index f9b1032f3bcfe9156e5585f49aef6bb90bb80cc5..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_1.m
+++ /dev/null
@@ -1,23 +0,0 @@
-% exercise 4.2.1
-
-% Number of samples
-N = 200; 
-
-% Mean
-mu = 17;       
-
-% Standard deviation
-s = 2;  
-
-% Number of bins in histogram
-NBins = 20;
-
-%% Generate samples from the Normal distribution
-X = normrnd(mu, s, N, 1);
-
-%% Plot a histogram
-mfig('Normal distribution');
-subplot(1,2,1);
-plot(X, 'x');
-subplot(1,2,2);
-hist(X, NBins);
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_2.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_2.m
deleted file mode 100644
index 285a701e92cec0420a6b668017eb362aa0557521..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_2.m
+++ /dev/null
@@ -1,30 +0,0 @@
-% exercise 4.2.2
-
-% Number of samples
-N = 100; 
-
-% Mean
-mu = 17;       
-
-% Standard deviation
-s = 2;  
-
-% Number of bins in histogram
-NBins = 10;
-
-%% Generate samples from the Normal distribution
-X = normrnd(mu, s, N, 1);
-
-%% Plot a histogram
-mfig('Normal distribution');
-subplot(1,2,1);
-plot(X, 'x');
-subplot(1,2,2);
-hist(X, NBins);
-
-%% Compute empirical mean and standard deviation
-mu_ = mean(X);
-s_ = std(X);
-
-display(mu_);
-display(s_);
\ No newline at end of file
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_3.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_3.m
deleted file mode 100644
index 5c93ad6640f4cdceca9ab61c22c1cd2f71a2e7e2..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_3.m
+++ /dev/null
@@ -1,31 +0,0 @@
-% exercise 4.2.3
-
-% Number of samples
-N = 1000; 
-
-% Mean
-mu = 17;       
-
-% Standard deviation
-s = 2;  
-
-% Number of bins in histogram
-NBins = 50;
-
-%% Generate samples from the Normal distribution
-X = normrnd(mu, s, N, 1);
-
-% Plot a histogram
-mfig('Normal distribution'); clf; hold all;
-[n, x] = hist(X, NBins);
-bar(x, n/N./gradient(x));
-x = linspace(min(x), max(x), 1000);
-plot(x, normpdf(x, mu, s), 'r', 'LineWidth', 5);
-xlim([min(x), max(x)]);
-
-%% Compute empirical mean and standard deviation
-mu_ = mean(X);
-s_ = std(X);
-
-display(mu_);
-display(s_);
\ No newline at end of file
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_4.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_4.m
deleted file mode 100644
index 5f6a12b0eba50b647a93d88083e3b2f05ff2f855..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_4.m
+++ /dev/null
@@ -1,13 +0,0 @@
-% exercise 4.2.4
-
-% Number of samples
-N = 1000; 
-
-% Mean
-mu = [13 17];       
-
-% Covariance matrix
-S = [4 3;3 9];  
-
-%% Generate samples from the Normal distribution
-X = mvnrnd(mu, S, N);
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_5.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_5.m
deleted file mode 100644
index 81bdf53d99994bd7996ae6efdd5bf983f214c170..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_5.m
+++ /dev/null
@@ -1,44 +0,0 @@
-% exercise 4.2.5
-
-% Number of samples
-N = 1000; 
-
-% Mean
-mu = [13 17];       
-
-% Standard deviation of x1
-s1 = 2;
-
-% Standard deviation of x2
-s2 = 3;
-
-% Correlation between x1 and x2
-corr = 0;
-
-% Covariance matrix
-S = [s1^2 corr*s1*s2;corr*s1*s2 s2^2];
-
-% Number of bins in histogram
-NBins = 20;
-
-%% Generate samples from the Normal distribution
-X = mvnrnd(mu, S, N);
-
-%% Plot scatter plot of data
-mfig('2-D Normal distribution'); clf;
-
-subplot(1,2,1);
-plot(X(:,1), X(:,2), 'x');
-axis equal;
-xlabel('x_1'); ylabel('x_2');
-title('Scatter plot of data');
-
-subplot(1,2,2);
-[n, x] = hist2d(X, NBins);
-imagesc(x(1,:), x(2,:), n);
-axis equal;
-axis xy;
-colorbar('South');
-colormap(1-gray);
-xlabel('x_1'); ylabel('x_2');
-title('2D histogram');
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_6.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_6.m
deleted file mode 100644
index 39b8a5b485874e6c7fba1bc15f12440d4b3bf54b..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_6.m
+++ /dev/null
@@ -1,40 +0,0 @@
-%% exercise 4.2.6
-
-% Digits to include in analysis (to include all, n = 1:10);
-n = [1];
-
-%% Load data
-cdir = fileparts(mfilename('fullpath')); 
-load(fullfile(cdir,'../Data/zipdata.mat'));
-
-% Extract digits
-X = traindata(:,2:end);
-y = traindata(:,1);
-classNames = {'0';'1';'2';'3';'4';'5';'6';'7';'8';'9';'10'};
-classLabels = classNames(y+1);
-
-% Remove digits that are not to be inspected
-j = ismember(y, n);
-X = X(j,:);
-classLabels = classLabels(j);
-classNames = classNames(n+1);
-y = cellfun(@(str) find(strcmp(str, classNames)), classLabels)-1;
-
-%% Compute mean, standard deviations, and covariance matrix 
-mu = mean(X);
-s = std(X);
-S = cov(X);
-
-%% Plot result
-mfig('Digits: Mean and std'); clf;
-subplot(1,2,1);
-I = reshape(mu, [16,16])';
-imagesc(I);
-axis image off
-title('Mean');
-subplot(1,2,2);
-I = reshape(s, [16,16])';
-imagesc(I);
-axis image off
-title('Standard deviation');
-colormap(1-gray);
diff --git a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_7.m b/exercises/02450Toolbox_Matlab/Scripts/ex4_2_7.m
deleted file mode 100644
index ffa461539c29470c9860637d0779d804b0e48c26..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Matlab/Scripts/ex4_2_7.m
+++ /dev/null
@@ -1,53 +0,0 @@
-%% exercise 4.2.7
-
-% Digits to include in analysis (to include all, n = 1:10);
-n = [1];
-
-%% Load data
-cdir = fileparts(mfilename('fullpath')); 
-load(fullfile(cdir,'../Data/zipdata.mat'));
-
-% Extract digits
-X = traindata(:,2:end);
-y = traindata(:,1);
-classNames = {'0';'1';'2';'3';'4';'5';'6';'7';'8';'9';'10'};
-classLabels = classNames(y+1);
-
-% Remove digits that are not to be inspected
-j = ismember(y, n);
-X = X(j,:);
-classLabels = classLabels(j);
-classNames = classNames(n+1);
-y = cellfun(@(str) find(strcmp(str, classNames)), classLabels)-1;
-
-%% Compute mean, standard deviations, and covariance matrix 
-mu = mean(X);
-s = std(X);
-S = cov(X);
-
-%% Generate 10 images with same mean and standard deviation
-Xgen = normrnd(repmat(mu,10,1), repmat(s,10,1));
-
-%% Plot images generated using the Normal distribution
-mfig('Digits: 1-D Normal');
-for k = 1:10
-    subplot(2,5,k);
-    I = reshape(Xgen(k,:), [16,16])';
-    imagesc(I);
-    axis image off
-end
-colormap(1-gray);
-
-
-%% Generate 10 images with same mean and covariance matrix
-Xgen = mvnrnd(mu, S, 10);
-
-%% Plot images generated using the Normal distribution
-mfig('Digits: Multivariate Normal');
-for k = 1:10
-    subplot(2,5,k);
-    I = reshape(Xgen(k,:), [16,16])';
-    imagesc(I);
-    axis image off
-end
-colormap(1-gray);
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py b/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
index c8d1a84c068b076f9404930d77148657dc553a89..ec2f3488eeca721c9cc8a84dd0e2c22f61a151af 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
@@ -1,3 +1,29 @@
 # exercise 4.1.1
-# Content to be added on discrete probability
-# Will be distrbuted as hotfix (and via git)
+import numpy as np
+import matplotlib.pyplot as plt 
+
+# Number of samples
+N = 200
+
+# Mean
+mu = 17
+
+# Standard deviation
+s = 2
+
+# Number of bins in histogram
+nbins = 20
+
+# Generate samples from the Normal distribution
+X = np.random.normal(mu, s, N).T
+# or equally:
+X = np.random.randn(N).T * s + mu
+
+# Plot the samples and histogram
+plt.figure(figsize=(12, 4))
+plt.title("Normal distribution")
+plt.subplot(1, 2, 1)
+plt.plot(X, ".")
+plt.subplot(1, 3, 3)
+plt.hist(X, bins=nbins)
+plt.show()
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_1.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_1.py
deleted file mode 100644
index 38a3f194614ce59fd7ff4a3b966ecb16de9d8193..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_1.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# exercise 4.2.1
-import numpy as np
-import matplotlib.pyplot as plt 
-
-# Number of samples
-N = 200
-
-# Mean
-mu = 17
-
-# Standard deviation
-s = 2
-
-# Number of bins in histogram
-nbins = 20
-
-# Generate samples from the Normal distribution
-X = np.random.normal(mu, s, N).T
-# or equally:
-X = np.random.randn(N).T * s + mu
-
-# Plot the samples and histogram
-plt.figure(figsize=(12, 4))
-plt.title("Normal distribution")
-plt.subplot(1, 2, 1)
-plt.plot(X, ".")
-plt.subplot(1, 3, 3)
-plt.hist(X, bins=nbins)
-plt.show()
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_2.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_2.py
deleted file mode 100644
index 39cf9bde37d77bc14b52cbcc95a9fde25ee3ff4f..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_2.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# exercise 4.2.2
-
-import numpy as np
-import matplotlib.pyplot as plt 
-
-# Number of samples
-N = 200
-
-# Mean
-mu = 17
-
-# Standard deviation
-s = 2
-
-# Number of bins in histogram
-nbins = 20
-
-# Generate samples from the Normal distribution
-X = np.random.normal(mu, s, N).T
-# or equally:
-X = np.random.randn(N).T * s + mu
-
-
-# Compute empirical mean and standard deviation
-mu_ = X.mean()
-s_ = X.std(ddof=1)
-
-print("Theoretical mean: ", mu)
-print("Theoretical std.dev.: ", s)
-print("Empirical mean: ", mu_)
-print("Empirical std.dev.: ", s_)
-
-# Plot the samples and histogram
-plt.figure()
-plt.title("Normal distribution")
-plt.subplot(1, 2, 1)
-plt.plot(X, "x")
-plt.subplot(1, 2, 2)
-plt.hist(X, bins=nbins)
-plt.show()
-
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_3.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_3.py
deleted file mode 100644
index e61c3cf33efef1b4b3aaa2b457f2f8556b1afa9d..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_3.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# exercise 4.2.3
-import numpy as np
-import matplotlib.pyplot as plt 
-from scipy import stats
-
-# Number of samples
-N = 500
-
-# Mean
-mu = 17
-
-# Standard deviation
-s = 2
-
-# Number of bins in histogram
-nbins = 20
-
-# Generate samples from the Normal distribution
-X = np.random.normal(mu, s, N).T
-# or equally:
-X = np.random.randn(N).T * s + mu
-
-# Plot the histogram
-f = plt.figure()
-plt.title("Normal distribution")
-plt.hist(X, bins=nbins, density=True)
-
-# Over the histogram, plot the theoretical probability distribution function:
-x = np.linspace(X.min(), X.max(), 1000)
-pdf = stats.norm.pdf(x, loc=17, scale=2)
-plt.plot(x, pdf, ".", color="red")
-
-# Compute empirical mean and standard deviation
-mu_ = X.mean()
-s_ = X.std(ddof=1)
-
-print("Theoretical mean: ", mu)
-print("Theoretical std.dev.: ", s)
-print("Empirical mean: ", mu_)
-print("Empirical std.dev.: ", s_)
-
-plt.show()
-
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_4.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_4.py
deleted file mode 100644
index 2db6dac00758edb1c945a8fefcb1e98e00172c08..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_4.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# exercise 4.2.4
-
-import numpy as np
-
-# Number of samples
-N = 1000
-
-# Mean
-mu = np.array([13, 17])
-
-# Covariance matrix
-S = np.array([[4, 3], [3, 9]])
-
-# Generate samples from the Normal distribution
-X = np.random.multivariate_normal(mu, S, N)
-
-print("Ran Exercise 4.2.4")
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_5.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_5.py
deleted file mode 100644
index 1d1bea4f9dfff7c59de9ab38446e67b305cb1c73..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_5.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# exercise 4.2.5
-
-import numpy as np
-import matplotlib.pyplot as plt 
-
-# Number of samples
-N = 1000
-
-# Standard deviation of x1
-s1 = 2
-
-# Standard deviation of x2
-s2 = 3
-
-# Correlation between x1 and x2
-corr = 0.5
-
-# Covariance matrix
-S = np.matrix([[s1 * s1, corr * s1 * s2], [corr * s1 * s2, s2 * s2]])
-
-# Mean
-mu = np.array([13, 17])
-
-# Number of bins in histogram
-nbins = 20
-
-# Generate samples from multivariate normal distribution
-X = np.random.multivariate_normal(mu, S, N)
-
-
-# Plot scatter plot of data
-plt.figure(figsize=(12, 8))
-plt.suptitle("2-D Normal distribution")
-
-plt.subplot(1, 2, 1)
-plt.plot(X[:, 0], X[:, 1], "x")
-plt.xlabel("x1")
-plt.ylabel("x2")
-plt.title("Scatter plot of data")
-
-plt.subplot(1, 2, 2)
-x = np.histogram2d(X[:, 0], X[:, 1], nbins)
-plt.imshow(x[0], cmap=plt.cm.gray_r, interpolation="None", origin="lower")
-plt.colorbar()
-plt.xlabel("x1")
-plt.ylabel("x2")
-plt.xticks([])
-plt.yticks([])
-plt.title("2D histogram")
-
-plt.show()
-
-
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_6.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_6.py
deleted file mode 100644
index 05f9319dbd350d4faaec2fe0d83dcc4364d16511..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_6.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# exercise 4.2.6
-import importlib_resources
-import numpy as np
-import scipy.linalg as linalg
-import matplotlib.pyplot as plt 
-from scipy.io import loadmat
-
-filename = importlib_resources.files("dtuimldmtools").joinpath("data/zipdata.mat")
-# Digits to include in analysis (to include all: n = range(10))
-n = [0]
-
-# Load Matlab data file to python dict structure
-# and extract variables of interest
-traindata = loadmat(filename)["traindata"]
-X = traindata[:, 1:]
-y = traindata[:, 0]
-N, M = X.shape
-C = len(n)
-
-# Remove digits that are not to be inspected
-class_mask = np.zeros(N).astype(bool)
-for v in n:
-    cmsk = y == v
-    class_mask = class_mask | cmsk
-X = X[class_mask, :]
-y = y[class_mask]
-N = np.shape(X)[0]
-
-mu = X.mean(axis=0)
-s = X.std(ddof=1, axis=0)
-S = np.cov(X, rowvar=0, ddof=1)
-
-plt.figure()
-plt.subplot(1, 2, 1)
-I = np.reshape(mu, (16, 16))
-plt.imshow(I, cmap=plt.cm.gray_r)
-plt.title("Mean")
-plt.xticks([])
-plt.yticks([])
-plt.subplot(1, 2, 2)
-I = np.reshape(s, (16, 16))
-plt.imshow(I, cmap=plt.cm.gray_r)
-plt.title("Standard deviation")
-plt.xticks([])
-plt.yticks([])
-
-plt.show()
-
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_2_7.py b/exercises/02450Toolbox_Python/Scripts/ex4_2_7.py
deleted file mode 100644
index f9d62f0844155350c01867801ecf1e5312429a73..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_Python/Scripts/ex4_2_7.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# exercise 4.1.7
-
-import importlib_resources
-import numpy as np
-import matplotlib.pyplot as plt 
-from scipy.io import loadmat
-
-filename = importlib_resources.files("dtuimldmtools").joinpath("data/zipdata.mat")
-# Digits to include in analysis (to include all, n = range(10) )
-n = [1]
-
-# Number of digits to generate from normal distributions
-ngen = 10
-
-# Load Matlab data file to python dict structure
-# and extract variables of interest
-traindata = loadmat(filename)["traindata"]
-X = traindata[:, 1:]
-y = traindata[:, 0]
-N, M = np.shape(X)  # or X.shape
-C = len(n)
-
-# Remove digits that are not to be inspected
-class_mask = np.zeros(N).astype(bool)
-for v in n:
-    cmsk = y == v
-    class_mask = class_mask | cmsk
-X = X[class_mask, :]
-y = y[class_mask]
-N = np.shape(X)[0]  # or X.shape[0]
-
-mu = X.mean(axis=0)
-s = X.std(ddof=1, axis=0)
-S = np.cov(X, rowvar=0, ddof=1)
-
-# Generate 10 samples from 1-D normal distribution
-Xgen = np.random.randn(ngen, 256)
-for i in range(ngen):
-    Xgen[i] = np.multiply(Xgen[i], s) + mu
-
-# Plot images
-plt.figure()
-for k in range(ngen):
-    plt.subplot(2, int(np.ceil(ngen / 2.0)), k + 1)
-    I = np.reshape(Xgen[k, :], (16, 16))
-    plt.imshow(I, cmap=plt.cm.gray_r)
-    plt.xticks([])
-    plt.yticks([])
-    if k == 1:
-        plt.title("Digits: 1-D Normal")
-
-
-# Generate 10 samples from multivariate normal distribution
-Xmvgen = np.random.multivariate_normal(mu, S, ngen)
-# Note if you are investigating a single class, then you may get:
-# """RuntimeWarning: covariance is not positive-semidefinite."""
-# Which in general is troublesome, but here is due to numerical imprecission
-
-
-# Plot images
-plt.figure()
-for k in range(ngen):
-    plt.subplot(2, int(np.ceil(ngen / 2.0)), k + 1)
-    I = np.reshape(Xmvgen[k, :], (16, 16))
-    plt.imshow(I, cmap=plt.cm.gray_r)
-    plt.xticks([])
-    plt.yticks([])
-    if k == 1:
-        plt.title("Digits: Multivariate Normal")
-
-plt.show()
-
diff --git a/exercises/02450Toolbox_R/Scripts/ex2_1_1.R b/exercises/02450Toolbox_R/Scripts/ex2_1_1.R
deleted file mode 100644
index 2d7d725bf4a2cf7d7d7944c89107d40fef06c008..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex2_1_1.R
+++ /dev/null
@@ -1,17 +0,0 @@
-####################
-# Exercise 2.1.1
-####################
-
-rm(list = ls()) # Clear work space
-
-x <- c(-0.68, -2.11, 2.39, 0.26, 1.46, 1.33, 1.03, -0.41, -0.33, 0.47)
-
-mean(x)
-sd(x)
-median(x)
-diff(range(x))
-
-# Range returns the minimum and maximum of the vector x.
-# To get the range, we must take the maximum minus the minimum.
-# We do this using the function diff, which finds differences
-# between consecutive elements in a vector.
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_1.R b/exercises/02450Toolbox_R/Scripts/ex4_2_1.R
deleted file mode 100644
index 1d8395b9320787960c4237b32b3499f29a903eb5..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_1.R
+++ /dev/null
@@ -1,26 +0,0 @@
-####################
-# Exercise 4.2.1
-####################
-rm(list = ls()) # Clear work space
-
-# Number of samples
-N <- 100
-
-# Mean
-mu <- 17
-
-# Standard deviation
-s <- 2
-
-# Number of bins in histogram
-NBins <- 20
-
-# Generate samples from the Normal distribution
-X <- rnorm(N, mean = mu, sd = s)
-
-# Plot a histogram
-{
-  par(mfrow = c(1, 2))
-  plot(X, main = "Data")
-  hist(X, breaks = NBins, main = "Histogram of Data")
-}
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_2.R b/exercises/02450Toolbox_R/Scripts/ex4_2_2.R
deleted file mode 100644
index ad847dd2523e5b0e0ea399e0c26c96476b994153..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_2.R
+++ /dev/null
@@ -1,31 +0,0 @@
-####################
-# Exercise 4.2.2
-####################
-rm(list = ls()) # Clear work space
-
-# Number of samples
-N <- 100
-
-# Mean
-mu <- 17
-
-# Standard deviation
-s <- 2
-
-# Number of bins in histogram
-NBins <- 20
-
-# Generate samples from the Normal distribution
-X <- rnorm(N, mean = mu, sd = s)
-
-# Plot a histogram
-{
-  par(mfrow = c(1, 2))
-  plot(X, main = "Generated data")
-  hist(X, breaks = NBins, main = "Histogram of generated data")
-}
-
-# Compute empirical mean and standard deviation
-(mu_ <- mean(X))
-(s_ <- sd(X))
-
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_3.R b/exercises/02450Toolbox_R/Scripts/ex4_2_3.R
deleted file mode 100644
index 9aac8b2801a00518f685aacba63ea88ba99507bc..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_3.R
+++ /dev/null
@@ -1,33 +0,0 @@
-####################
-# Exercise 4.2.3
-####################
-rm(list = ls()) # Clear work space
-
-# Number of samples
-N <- 1000
-
-# Mean
-mu <- 17
-
-# Standard deviation
-s <- 2
-
-# Number of bins in histogram
-NBins <- 50
-
-# Generate samples from the Normal distribution
-X <- rnorm(N, mean = mu, sd = s)
-
-# Plot a histogram
-{
-  par(mfrow=c(1,1))
-  res <- hist(X, breaks = NBins, freq = FALSE)
-  x <- res$mids
-  x <- seq(from = min(x), to = max(x), length.out = 1000)
-  lines(x, dnorm(x, mean = mu, sd = s))
-}
-
-# Compute empirical mean and standard deviation
-(mu_ <- mean(X))
-(s_ <- sd(X))
-
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_4.R b/exercises/02450Toolbox_R/Scripts/ex4_2_4.R
deleted file mode 100644
index d717df0f6ff06a7cfe6445146b14944b20c9ba26..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_4.R
+++ /dev/null
@@ -1,24 +0,0 @@
-####################
-# Exercise 4.2.4
-####################
-rm(list = ls()) # Clear work space
-
-# Library for multivariate normal distribution
-library(MASS) # install.packages("MASS")
-?mvrnorm
-
-# Number of samples
-N <- 1000
-
-# Mean
-mu <- c(13, 17)
-
-# Covariance matrix
-S <- matrix(c(4, 3, 3, 9), nrow = 2, byrow = TRUE)
-
-# Generate samples from the Normal distribution
-X <- mvrnorm(N, mu = mu, Sigma = S)
-
-# Inspect the dimensions of the matrix containing
-# the generated multivariate normal vectors.
-dim(X)
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_5.R b/exercises/02450Toolbox_R/Scripts/ex4_2_5.R
deleted file mode 100644
index 30daa0289bfc267d7590384d4667b6734b014e59..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_5.R
+++ /dev/null
@@ -1,45 +0,0 @@
-####################
-# Exercise 4.2.5
-####################
-rm(list = ls()) # Clear work space
-
-# load the package "gplots", which contains the function hist2d for making 2-dimensional histograms. If the package is not already installed on your computer, an error will result from the function call library(gplots). In that case, install the package using install.packages("gplots") and then run library(gplots) again. Same for the package MASS.
-#library(gplots)
-
-library(MASS)
-# Number of samples
-N <- 1000
-
-# Mean
-mu <- c(13, 17)
-
-# Standard deviation of x1
-s1 <- 2
-
-# Standard deviation of x2
-s2 <- 3
-
-# Correlation between x1 and x2
-corr <- 0
-
-# Covariance matrix
-S <- matrix(c(s1^2, corr * s1 * s2, corr * s1 * s2, s2^2), nrow = 2, byrow = TRUE)
-
-# Number of bins in histogram
-NBins <- 20
-
-# Generate samples from the Normal distribution
-X <- mvrnorm(N, mu = mu, Sigma = S)
-
-# Plot scatter plot of data
-{
-  xrange <- mu[1] + S[1, 1] * c(-3, 3)
-  yrange <- mu[2] + S[2, 2] * c(-3, 3)
-  par(mfrow = c(1, 2))
-  plot(xrange, yrange, type = "n", ylab = "x2", xlab = "x1",
-       main = "Scatter plot of data")
-  points(X[, 1], X[, 2])
-  k <- kde2d(X[,1], X[,2])
-  image(k, col = gray(32:0 / 32),
-        main = "2-D Normal distribution", xlab = "x1", ylab = "x2")
-}
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_6.R b/exercises/02450Toolbox_R/Scripts/ex4_2_6.R
deleted file mode 100644
index 462b9d19db6107eaf361c2df69e0db61b8de0c75..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_6.R
+++ /dev/null
@@ -1,59 +0,0 @@
-####################
-# Exercise 4.2.6
-####################
-rm(list = ls()) # Clear work space
-
-library(MASS)
-
-# Digits to include in analysis (to include all, n = 1:10)
-n <- c(1) # c(1,5,9)
-n <- sort(n)
-
-# Load the library R.matlab to enable the function readMat,
-# which allows R to read the matlab .mat format.
-library(R.matlab) # install.packages("R.matlab")
-
-# The row of training data that we will look at
-i <- 1
-
-# Read in the data
-data <- readMat(file.path("Data", "zipdata.mat"))
-
-# Check that the structure data contains two matrices, testdata and traindata
-names(data)
-
-ncols <- ncol(data$traindata)
-
-# Extract digits
-X <- data$traindata[, 2:ncols]
-y <- data$traindata[, 1]
-classNames <- c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10")
-classLabels <- classNames[y + 1]
-
-# Remove digits that are not to be inspected
-j <- match(y, n)
-X <- X[!is.na(j), ]
-classLabels <- classLabels[!is.na(j)]
-classNames <- classNames[n + 1]
-y <- y[!is.na(j)]
-for (k in 0:(length(n) - 1))
-{
-  classlab <- n[k + 1]
-  y[y == classlab] <- k
-}
-
-# Compute mean, standard deviations, and covariance matrix
-mu <- colMeans(X)
-s <- apply(X, 2, sd)
-S <- cov(X)
-
-# Plot result
-{
-  par(mfrow = c(1, 2))
-  I <- mu
-  dim(I) <- c(16, 16)
-  image(I[, 16:1], main = "Digits: Mean", col = gray(32:0 / 32))
-  I <- s
-  dim(I) <- c(16, 16)
-  image(I[, 16:1], main = "Digits: SD", col = gray(32:0 / 32))
-}
diff --git a/exercises/02450Toolbox_R/Scripts/ex4_2_7.R b/exercises/02450Toolbox_R/Scripts/ex4_2_7.R
deleted file mode 100644
index ad3cf1f8e8bca22ad16a8beb4f8532c0d98986bd..0000000000000000000000000000000000000000
--- a/exercises/02450Toolbox_R/Scripts/ex4_2_7.R
+++ /dev/null
@@ -1,74 +0,0 @@
-####################
-# Exercise 4.2.7
-####################
-rm(list = ls()) # Clear work space
-
-library(MASS)
-
-# Digits to include in analysis (to include all, n = 1:10)
-n <- c(1) # c(1,5,9)
-n <- sort(n)
-
-# Load the library R.matlab to enable the function readMat,
-# which allows R to read the matlab .mat format.
-library(R.matlab) # install.packages("R.matlab")
-
-# The row of training data that we will look at
-i <- 1
-
-# Read in the data
-data <- readMat(file.path("Data", "zipdata.mat"))
-
-# Check that the structure data contains two matrices, testdata and traindata
-names(data)
-
-ncols <- ncol(data$traindata)
-
-# Extract digits
-X <- data$traindata[, 2:ncols]
-y <- data$traindata[, 1]
-classNames <- c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10")
-classLabels <- classNames[y + 1]
-
-# Remove digits that are not to be inspected
-j <- match(y, n)
-X <- X[!is.na(j), ]
-classLabels <- classLabels[!is.na(j)]
-classNames <- classNames[n + 1]
-y <- y[!is.na(j)]
-for (k in 0:(length(n) - 1))
-{
-  classlab <- n[k + 1]
-  y[y == classlab] <- k
-}
-
-# Compute mean, standard deviations, and covariance matrix
-mu <- colMeans(X)
-s <- apply(X, 2, sd)
-S <- cov(X)
-
-# Generate 10 images with same mean and standard deviation
-Xgen <- mvrnorm(n = 10, mu = mu, Sigma = diag(s))
-
-# Plot images generated using the Normal distribution
-{
-par(mfrow = c(2, 3))
-  for (k in 1:6) {
-    I <- Xgen[k, ]
-    dim(I) <- c(16, 16)
-    image(I[, 16:1], main = "Digits: 1-D Normal", col = gray(32:0 / 32))
-  }
-}
-
-
-# Generate 10 images with same mean and covariance matrix
-Xgen <- mvrnorm(n = 10, mu = mu, Sigma = S)
-
-{
-  par(mfrow = c(2, 3))
-  for (k in 1:6) {
-    I <- Xgen[k, ]
-    dim(I) <- c(16, 16)
-    image(I[, 16:1], main = "Digits: Multivariate Normal", col = gray(32:0 / 32))
-  }
-}