Reduction in
action
Practical DP & NPC Problems
CSCI 3160 Tutorial 11
Nov 16th, 2016
Knapsack problem
Unbounded knapsack problem:
The supply of each item is unbounded.
Item
Weight / w
Value / v
1
6
30
2
3
14
3
4
16
4
2
9
Optimal solution: 1x Item 1 + 2x I
Solution to Assignment 3
Chen Zitong
Ex 5.9
(a)If graph G has more than |V|-1 edges, and there
is a unique heaviest edge, then this edge cannot be
part of a minimum spanning tree
(False, e can be a bridge)
(b) If G has a cycle with a unique heaviest edge
CSCI3160 Tutorial (9th week)
Midterm
L AI Qiuxia, SHB 905,Email:
[email protected]
Offi ce hour: Tue 10:00am-12:00pm
Outlines
Q1
Q2
Q3
Q4
Q5
Q6
2
Q1
(a)
True.
Suppose
=
3
Q1
The product of two n-digit integers can be computed
(b)
in time using
Homework 2
Solution Guide / Tips
CSCI 3160 Tutorial 7
Oct 19th, 2016
Pouring Water
?
0/10
7/7
4/4
?/10
?
2/7
?/4
or
?
?
?/10
?/7
So the problem is about states and transitions.
State: volumes of the containers <Va,Vb,Vc>
Transitions: the pouring operation
Homework 1
CHEN Zitong
2.4
A
dividing into five sub problems of half the siz
e, recursively solving , then combining in linear t
me.
T (n) = 5T (n/2) + O(n)
a = 5, b = 2, d = 1
2.4
B
recursively solving two sub problems of size
n 1 and then combining
BFS / DFS
And its applications
CSCI 3160 Tutorial3
Sep 21st, 2016
DFS
Search the unvisited nodes as deep as possible.
Pass 1:
1 2 3 4 6
# go back
visited
# go back
visited
# go back
visited
# go back
to 4, all neighbors
to 3, all neighbors
to 2, all neigh
CSCI3320: Homework 2, Spring 2017
Teacher: John C.S. Lui
SAMPLING THEORY
209
1. Sampling DistributionELEMENTARY
of means. A population
consists of the five numbers 2, 3, 6, 8,
and
CHAP. 8]
11. Consider all possible samples of size 2 that can be drawn with
CSCI3320: Homework 01, Spring 2017
Teacher: John C.S. Lui
1. Assume we are given the task of building a system to distinguish junk email. What is in a junk
email that lets us know that it is junk? How can the computer detect junk through a syntactic
analy
Course outline (the secon half term)
Part 8 Basic issues
!The large number law and statistical consistency
!Bias-Variance Tradeoff
!Five model selection related topics
!Major streams of model selection studies
!Two approximate implementations of integral
Machine(Learning(Theory(
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Department(of(Computer(Science(and(Engineering,(
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!
Dean's reminder
1) To brief students about the "Student/Faculty Expectations on Teaching and
Lea
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(
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h1p:/www.cse.cuhk.edu.hk/~lxu/(
(
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(
!
Dean's reminder
1) To brief students about the "Student/Faculty Expectations on Teaching and
Lea
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(
Lei(Xu(
h1p:/www.cse.cuhk.edu.hk/~lxu/(
(
Department(of(Computer(Science(and(Engineering,(
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!
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1) To brief students about the "Student/Faculty Expectations on Teaching and
Lea
Course outline (the secon half term)
Part%8%Basic&issues&
!The large number law and statistical consistency
!Bias-Variance Tradeoff
!Five model selection related topics
!Major streams of model selection studies
!Two approximate implementations of integral
Assignment 3 of CSCI5030 - Machine Learning Theory
Deadline: 14:30pm, 19 Nov., 2014
Assignment 3
1. Given two Gaussians G(x|1 , 1 ), G(x|2 , 2 ), and a set of i.i.d. samples X = cfw_xt N
t=1 , with each xt coming from
either G(x|1 , 1 ) with a prior proba
Assignment 4 of CSCI5030 - Machine Learning Theory
Deadline: 14:30pm, 10 Dec., 2014
Assignment 4
1. The Fisher information measures the amount of information that an observable random variable X carries about
an unknown parameters upon which the probabili
Assignment 2 of CSCI5030 - Machine Learning Theory
Deadline: 14:30pm, 22 Oct., 2014
Assignment 2
1. A learning system consists of three basic ingredients, namely, Learner, Theory and Implementation.
(1.1): Please describe the key points for each ingredien
Assignment 1 of CSCI5030 - Machine Learning Theory
Deadline: 14:30pm, 8 Oct., 2014
Assignment 1
1. Let the random variable x have continuous cumulative distribution function F (x) =
Rx
p(x)dx, and define the
random variable y as y = F (x), please prove th
Assignment 5 of CSCI5030 - Machine Learning Theory
Deadline: 14:30pm, 12 Dec., 2014
Assignment 5
1. Kullback-Leibler (KL) divergence is a non-symmetric measure of difference between two probability distribution
p(X) and q(X). The KL divergence of q(X) fro
CSCI3160-15F CSE-CUHK-HK-CHN
Design and Analysis of Algorithms
Homework 4
Due: 5pm Dec 7, 2015
1. Exercises 8.4.
2. Use the restriction method to prove the NP-completeness of the following problems:
(a) Tree Subgraph
Instance: Graph G and tree T .
Questio
#include<stdio.h>
#include<string.h>
#include<ctype.h>
int check(char x[]);
int main(void)cfw_
char add[50]=cfw_0;
printf("Enter email address: ");
gets(add);
int judge=check(add);
if(judge=0)
printf("This email address is not valid.");
else if(judge=1)
p
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig
import scipy.fftpack as fft
from mpl_toolkits.mplot3d import Axes3D
# EDIT STUFF HERE TO TEST #
Azimuth = 70 # Azimuth of incoming signal
Elevation = -33 # Elevation of incoming
Overview
Unilever is a British-Dutch multinational consumer goods company co-headquartered
in Rotterdam, Netherlands, and London, United Kingdom. Its products include food,
beverages, cleaning agents and personal care products. Originated in 1930s as a so
/*
THis program grows matrix from one of its corners.
*/
#include <stdio.h>
#define N 5
int main() cfw_
int mat[N][N] ;
int size, direction, i, j ;
printf("Directions:\n")
printf("0: growing from
printf("1: growing from
printf("2: growing from
printf("3: