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Summary CSE 575 Midterm Exam Cheat Sheet (2 pages)

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CSE 575 Statistical Machine Learning Midterm Exam Cheat Sheet(2 pages). This is allowed to have during the midterm and you can print it.Summary for week 1-4 - The coverage is until Week 4, but only until the SVM lectures (i.e., the Graphical Models are not included). Cheat Sheet used during the midterm exam with the help of which I scored 90% in the midterm exam.ASU CSE 575 midterm exam cheat sheet.

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ASU CSE 575 Statistical ML - Midterm Exam Cheat Sheet


Trace of a matrix M: tr(M ) = di=1 mii
P
Eigenvector x, eigenvalues λi : M x = λx ⇐⇒ (M − λI)x = 0
tr(M ) = di=1 λi , det(M ) = |M | = di=1 λi where d=diagonal
P Q
∂M x ∂y t x ∂xt M x
∂x
= M t, ∂x
= y, ∂x
= (M + M t )x
P (Hj )P (B|Hj )
Bayes Rule: P (Hj |B) = P∞ P (H )P (B|H ) , where prior prob.=P (Hj ) & posterior=P (Hj |B)
i i i
A, B = independent events ⇒P(AB)=P(A)P(B)
Probability Mass Fct.(PMF) to represent {p1 , p2 , ...pm }, P (X) =prob{X = x} tak-
P
ing m values from v = v1 , ...vm , P (X) ⩾
P0, x∈v P (x) = 1
expected value = mean = µ =PE[x] = x∈v xP (x), E[f (x)] = f (x)P (x), E=linear op-
2 2
erator, V ar[x] = E[(x −
Pµ) ] = (x − µ) P (x)
Covariance matrix = (symmetric) =E[(x-µ)(x−µ)t ], σxy = Cov(x, y) = E[(x−µx )(y−
µy )]
P (x,y) P (x)P (y/x)
Marginal distribution for x : Px (x) = y P (x, y), P (x|y)= P (y) = P P (x)P (y|x)
P
R∞ x
Continuous random variable PDF , p(x) ⩾ 0, −∞ p(x) dx=1
Rx
Cumulative distribution fct. CDF: F (X) = −∞ p(t) dt
....
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