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 # Exercise 1.1.1 Image convolution
 
-- Read the image using `skimage.io.imread`
-- For convolution use `scipy.ndimage.convolve`
-- After completing the exercise test agains `scipy.ndimage.gaussian_filter`
+- Read the image using [`skimage.io.imread`](https://scikit-image.org/docs/stable/api/skimage.io.html#skimage.io.imread)
+- For convolution use [`scipy.ndimage.convolve`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.convolve.html)
+- After completing the exercise test agains [`scipy.ndimage.gaussian_filter`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html)
   
 
 # Exercise 1.1.3 Curve smoothing
 
-- Make the matrix using `scipy.linalg.circulant`
-- For matrix-vector multiplication you can use a dedicated function from `numpy` or `@` operator
+- Make the matrix using [`scipy.linalg.circulant`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.circulant.html)
+- For matrix-vector multiplication you can use [`numpy.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) or the shorthand operator `@` (explained at the bottom of page dedicated to `matmul`).
   
 # Exericse 1.1.6 Working with volumetric image
-- To get hold of all image in a folder, use `sorted(os.listdir(\<FOLDER NAME\>))`
+- To get hold of all image in a certain folder, use `sorted(os.listdir(\<FOLDER NAME\>))`
 
 # Exercise 1.1.7 PCA of multispectral image
-- For eigen decomposition use ` np.linalg.eig`
-- You can compare your PCA agains the existing implementation `sklearn.decomposition.PCA`
+- For eigendecomposition use `numpy.linalg.eig`
+- You can compare your PCA against the existing implementation `sklearn.decomposition.PCA`
 
 # Exercise 1.1.8 Bacterial growth from movie frames
 - To read the movie as a list of images, use following code block