Lecture 1 Introduction to Data Structures and Algorithms 241 F19 Lecture 1.pdf · Lecture 1...

Lecture1IntroductiontoDataStructures

andAlgorithms

ECE241– AdvancedProgrammingIFall2019MikeZink

ECE 241 – Data Structures Fall 2019 © 2019 Mike Zink

Overview

1

• Introductionofdatastructuresandalgorithms• Informationaboutcourse


What is an algorithm?

• Well-defined computationalprocedure• Takessetofvaluesasinputandproduces setofvaluesasoutput

• Tooltosolvewelldefined computationalproblem


Algorithm example: Sorting numbers

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• Input: Asequence ofnnumbers: <a1, a2,…, an>• Output:Apermutation(reordering)<a’1,a’2,…,a’n>ofinputsequence suchthata’1 <=a’2 <=… <=a’n

• Input:<31,41,59,26,41,58>Output:<26,31,41,41,58,59>

• Sincesortingisessential, alargenumberofsortingalgorithmsexist


A Data-centric View of the World

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• Dataareimportantinmanydisciplines• DNAsequences, images,networktraffic,sensorreadings,maps,customerprofiles,etc.

• PastECEfocus:processing• Computationatcore:processors,computers,etc.

• NewECEfocus:data• Dataatcore:datacenters,datasecurity,etc.

• Needstructuredapproachtohandlingdata


What kind of problems are solved by algorithms?

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• HumanGenomeProject• Internet:Routing,searches, security• Electroniccommerce• Commercial enterprises• …


Human Genome Project

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• Completemappingandunderstanding ofallthegenesofhumanbeings

• ~20,500humangenes• ~3billionchemicalbasepairsthatmakeuphumanDNA

• Requiresalgorithmstostoreandanalyze


Internet

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• Routingalgorithmsthatfindshortestpathandadapttochanges

• Searchenginesthatfindpageswithparticularinformation

• Encrypted communication• Queuemanagement


Electronic Commerce

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• Adplacements• Recommendations: Netflix,Amazon,etc.• Hashingforauthentication• Electronictrading


Manufacturing and Commercial Enterprises

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• Allocationofscarceresources:• Assigningcrewstoflights• Routesforpackagedeliveries• Storageanddistributionofgoods


Why Data Structures?

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• Waytostoreandorganizedata• Facilitateaccessandmodificationofdata• Differentdatastructureshavestrengthsandweaknesses

• Bettersuitedforaspecificalgorithmthanothers


Data Structures and Algorithms

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• Datastructures• Representationandorganizationofdata

• Algorithms• Methodsforimplementingoperationsondatastructures

• Datastructuresandalgorithmsarecloselyrelated

• Datastructuresareoftentunedforcertainalgorithms


Big Data

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• Extremely largevolumesofdata• Canbecomputationallyanalyzed

• InternetofThings• Socialmediacontent

• Requiresalgorithmsanddatastructures toanalyze


Artificial Intelligence

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• Often”trained”onbigdatasets• Imagerecognition• Voicerecognition

• Reliesonalargesetofalgorithm,varyingincomplexity

• Linearregression• Statistics,probability


Complexity Problems

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• Datasetscanbevery large• Operationsmaytakealotoftimeormemory

• Efficientimplementationscanmakeabigdifference

• Examplesoflargedatasetsandsizes?


• Example: Million Song Dataset

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• http://labrosa.ee.columbia.edu/millionsong/• Datasetofsongs:title,artist,recordingyears,etc.

• Howwouldyoufigureout• Whichartisthasrecordedmostsongs?• Whichsonghasbeencoveredthemosttimes?

• Whatarethemostcommonwordsinatitle?

• Whatarepotentialproblems?


What will you learn in the course?

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• Howtothinkaboutdataandoperationsondata

• Howtodesigndatastructuresforefficientuse

• Howtodeterminetheefficiencyofalgorithms• Asetofcommondatastructuresandalgorithmsfortypical

operationsondata(e.g.,searching,sorting,etc.)

• Advancedprogrammingtechniques(e.g.,recursion,etc.)

• HowtoimplementalargersoftwareprojectwithaprofessionalIDE


Python

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• WewillusePythonasprogramminglanguage• Datastructuresandalgorithmsareindependentofprogramming

language• Thisisnotabasicprogrammingcourse

• Wefocusonhigherlevel issues• Youshouldalreadyhaveexperiencewithaprogramming

language(Python,Java,C/C++)• IDEusedinthiscourse:PyCharm

• Professional environment


PyCharm

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• IDEforPyhton• https://www.jetbrains.com/pycharm/• Veryimportanttobecome familiarwithtool!!• SeeintroslidesforPyCharm (moreduringthisweek’sdiscussion)


Course Organization

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• Coursewebsite:http://www.ecs.umass.edu/ece241/


Course Events

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• Eventstoattend• Lectures• Discussions

• Assignments• Homework• Projects

• Exams• 2examsduringsemester• 1finalexam


Course Policy

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• Readthesyllabus initsentirety!• Understandexpectations forthiscourse

• Deadlinesarepostedonwebsite• Noextensions forhomeworkorprojects

• AssignmentsandgradesmanagedthroughMoodle

• Nosharingofcode• Automatedcheckingofsubmissions


Office Hours

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• TA’s:• Tuesday’s 4PM- 5:30PM• Wednesday’s 3PM- 4PM

• Prof.Zink:• Friday’s9- 11PM


Properties of Data Structures and Algorithms

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Quality Measures for Data Structures

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• Whatdoyouexpectfromagooddatastructure?


Quality Metrics for Algorithms

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• Whatdoyouexpectfroma“good”algorithm?

Correctness

• Insomecasesabsolute• E.g.,whatisthesumofallvaluesinarray?

• Somealgorithmshavemultiplesolutions• Examples?

• Somealgorithmsmayhavesolutionswherecorrectness ishardtodefine• Examples?

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Multiple solutions

• Datasetfornumberofwinsbypitcher(2011):• {(Halladay,21),(Jiminez,19),(Lester,19),(Price,19),(Sabathia,21),(Wainwright,20)}

• Whichpitcherhasmostwins?27

Unclear “correctness”

• Makeimagesquare:

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?

Running time performance

• “Thealgorithmfinishes in12seconds.”• Howtoexpressperformancemoreaccurately?

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Memory use performance

• “Thedatastructurerequires727bytes.”• Howtoexpressmemoryrequirementsinamoremeaningfulway?

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It all depends…

• Qualityofdatastructuredepends onoperation• Example:unsorted“pile”ofpapers

• Veryfastforaddingitem• Notveryfastforfindingitem

• Example:paperssortedbylastname• Slowerforaddingitem• Fasterforfindingitem(ifsearchisbylastname)• However:alsoslowifsearchisbyfirstname

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Data types

• Whataretypicaldatatypestostoreinformation?

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Data types

• Common types• Numerictypes:integer,floatingpoint• Boolean• Texttypes:character,string• Compositetypes• References(“pointer”)

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Algorithms and data types• Manyalgorithmscanbeusedwithdifferentdatatypes

• Sortingofintegers• Sortingofstrings• Sortingofcompositetypes

• Mayrequiresmalladaptation incode• Differentcomparisonfunctions• Differentcodefor“movingdataaround”

• Whyisthispointimportant?• Idonotwantyoutoleavethiscoursesaying:“Useless.Weonlylearnedhowtosortnumbers!”

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Objects• Foralgorithmdiscussion, wecanthinkof“objects”inaverygeneralway

• Operationsonobjects• Creation(anddestruction)• Comparison

• Operationsonobjects indatastructures• Insertion• Deletion• Algorithm-specificoperations:search,sort,etc. 35


Next Steps

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• Nextlecture“AsymptoticNotationandMergeSort”onThursday

• FirstdiscussionalsoonThursday

• WillpostfirstHWonThursday

Lecture 1 Introduction to Data Structures and Algorithms 241 F19 Lecture 1.pdf · Lecture 1...

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