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Intro to Big Data Science: Assignment 1 Solution


Exercise 1 (Self-learning)

Log into “cookdata.cn”, and enroll the course “êâ‰Æ Ú”. The online homework is given there. The system will judge your answers.

Exercise 2 (Written Problem)

Given the ordered data {x(i )}2i˘n1¡1 data set is equal to the minimizer

with increasing order. Show that the median of the of the following L1 minimization problem:



2n¡1

min
X
x(n) ˘ arg

jx(i ) ¡cj.

c



i ˘1

Exercise 3 (Written Problem)

Consider the probability density function (PDF) shown in the following figure and equa-

tions:
p(x)

8

22x
if x
˙
0,

˘
>
0,










w ¡ w2 ,
if 0 É x É w,


>










<










>
0,



ifw ˙ x.


>








:

    1. Which of the following expression is true? (Only one truth.)
        (A) E[X ] ˘ R¡11( w2 ¡ w2x2 )dx;

        (B) E[X ] ˘ R¡11 x( w2 ¡ w2x2 )dx;





1











            (C) E[X ] ˘ R¡11 w( w2 ¡ w2x2 )dx;

            (D) E[X ] ˘ R0w ( w2 ¡ w2x2 )dx;

            (E) E[X ] ˘ R0w x( w2 ¡ w2x2 )dx;

            (F) E[X ] ˘ R0w w( w2 ¡ w2x2 )dx;
        2. What is P(x ˘ 1jw ˘ 2)?

        3. When w ˘ 2, what is p(1)? Exercise 4 (Written Problem)
Let X and Y be two continuous random variables. The conditional expectation of Y on X ˘ x is defined as the expectation of Y with respect to the conditional probability density p(Y jX ):

E(Y jX ˘ x) ˘ ZY
y p(yjX ˘ x)dy ˘
RY ypx (x)
,


p(x, y)dy






where px (x) is the marginal probability density of Y . Show the following properties of the conditional expectation:

        1. Epy Y ˘ Epx [E(Y jX )], where Epy means taking the expectation with respect to the marginal probability density py .

Remark: This formula is sometimes called the tower rule.

        2. If X and Y are independent, then E(Y jX ˘ x) ˘ E(Y ). Exercise 5 (Written Problem)
T
The Jaccard distance between two sets A and B is defined as J–(A, B) ˘ 1 ¡ jjAA  BBjj ˘

jA4Bj



Jaccard




, where jSj stands for the number of elements in the set S. Show that the
S

jA
S
Bj








is actually a distance, i.e., it satisfies the three properties:

distance J




    1. Positivity: J–(A, B) ˚ 0, and “=” if and only if A ˘ B;

    2. Symmetry: J–(A, B) ˘ J–(B, A);

    3. Triangle inequality: J–(A, B) É J–(A,C ) ¯ J–(B,C ).




2

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