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+Assignment 1
+
+- What is the kernel trick?
+Since we can rewrite the 𝐿^2 regularised linear regression formula to a form where non-linear transformations 𝝓(x) only appear via inner product, we do not have to design a 𝑑-dimensional vector 𝝓(x) and derive its inner product. Instead, we can just choose a kernel
+𝜅(x, x') directly where the kernel is the inner product of two non-linear input transformations according to:
+𝜅(x, x') = 𝝓(x)^T𝝓(x').
+This is known as the kernel trick:
+If x enters the model as 𝝓(x)^T𝝓(x') only, we can choose a kernel 𝜅(x, x') instead of chosing 𝝓(x). p. 194
+
+- In the literature, it is common to see a formulation of SVMs that makes use of a hyperparameter. What is the purpose of this hyperparameter?
+The purpose is to regularize. p. 211
+
+
+- In neural networks, what do we mean by mini-batch and epoch?
+We call a small subsample of data a mini-batch, which typically can contain 𝑛𝑏 = 10, 𝑛𝑏 = 100, or 𝑛𝑏 = 1 000
+data points. One complete pass through the training data is called an epoch, and consequently consists of 𝑛/𝑛𝑏 iterations. p. 125
+
+
+
 Assignment 4
 
 4.1