# CS代写 Machine Learning 机器学习 – cscodehelp代写

Machine Learning Wednesday, 19. January 2022 Prof. S. Harmeling DUE 23:55 Tuesday, 25. January 2022

Exercise set #12

Please submit your solutions in teams of two using the sciebo file-drop folder. The link is available in ILIAS. For the formatting please stick to the submission guideline.pdf that you can find on sciebo. In the case of multiple uploads we will consider the latest. Uploads after the deadline will be deleted without further notice.

1. Laplace approximation (from MacKay [1])

A photon counter is pointed at a remote star for one minute, in order to infer the rate of photons arriving at the counter per minute λ. Assuming the number of photons collected r has a Poisson distribution

p(r|λ) = exp(−λ) r! , with λ > 0

and assuming an improper1 prior

perform a Laplace approximation for the posterior of λ.

2. Gaussian processes with probit likelihood (from Rasmussen and Williams [2])

For a binary Gaussian process classification model, show the equivalence of using

(i) a noise-free latent process combined with a probit likelihood, i.e., choosing

p(yi = 1|fi) = σprobit(fi) = √2π

can be expressed by introducing additional noisy latent variables f ̃, which differ

from f by Gaussian noise, and defining p(y = 1|f ̃) as follows: iii

exp − 2 dt

(ii) a latent process with Gaussian noise combined with a step-function likelihood. This

exactly match (i)?

0 otherwise

Hint: Start with the expressions in (i) and integrate out f ̃ to get an expression for

p(yi = 1|fi), which should look like σprobit. What do you have to plug in for σ2 in (ii) to

1An improper prior is a prior that cannot be normalized to a probability distribution.

3. Kernel design

Recall from the lecture that a positive semidefinite kernel is a function k : X × X → R, such that, for every set x = {xi}i=1,…,N with xi ∈ X ∀i, the matrix kxx with elements kxx(ij) = k(xi,xj) is positive semidefinite. You can use the facts, that if k1(x,x′) and k2(x, x′) are both positive semidefinite kernels and α ∈ R, then the functions k(x, x′) = α2k1(x, x′), k(x, x′) = k1(x, x′)+k2(x, x′) and k(x, x′) = k1(x, x′)·k2(x, x′) are also positive semidefinite kernels. For each of the following functions, determine whether or not they are kernels. Clearly state your assumptions and your derivation.

(a) k(x1,x2) = C for C ∈ R>0 (b) k(x1,x2) = x1x2 for X = R

[1] C MacKay and C Mac Kay. Information theory, inference and learning algorithms. Cambridge university press, 2003.

[2] Rasmussen and . I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.