Wheeler, Travis J

English

University of Montana

University of MontanaScholarWorks at University of MontanaSyllabi Course SyllabiFall 9-1-2018CSCI 232.00: Data Structures and AlgorithmsTravis J. WheelerUniversity of Montana, travis.wheeler@umontana.eduLet us know how access to this document benefits you.Follow this and additional works at: https://scholarworks.umt.edu/syllabiThis Syllabus is brought to you for free and open access by the Course Syllabi at ScholarWorks at University of Montana. It has been accepted forinclusion in Syllabi by an authorized administrator of ScholarWorks at University of Montana. For more information, please contactscholarworks@mso.umt.edu.Recommended CitationWheeler, Travis J., "CSCI 232.00: Data Structures and Algorithms" (2018). Syllabi. 8060.https://scholarworks.umt.edu/syllabi/8060	 	 	 			 		 		 					 	 				 	 	 	 					 	 					 	 					 	 	 	 	 					 							 				 				 	 			 			 			 	 			 	 			 		 		 	 	 	 	 				 			 	 			 	 			 		 			 	 	 	 	 					 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 	 	CSCI 232: Data	 Structures and	 Algorithms Course 	information 	Fall 2018 Meeting time/location:	 Lecture:	 M/W 1:00-1:50PM Social	Sciences	356 Labs:	 W	 3:00-4:50 Liberal Arts 103B Th 11:00-12:50 Social	Sciences	344 F	 1:00-2:50 Social	Sciences	344 Course material/submissions/grades are in Moodle (http://umonline.umt.edu) Instructor 	information 	Instructor: 	Travis Wheeler Office: 	Social	Science 	420 E-mail: travis.wheeler@umontana.eduPhone:	406-243-6219 Office 	Hours: Mon	2:00-4:00 Wed 	10:30-12:00 Or 	by	 appointment (http://wheelerlab.org/schedule) Teaching Assistants:	David	 Rich Office: 	Social	Science 421 E-mail: david.rich@umconnect.umt.eduOffice 	Hours: Tues	 2:00-3:30	 - meet in SS 402 Jesse	 Davidson Office: 	Social	Science 422 E-mail: jesse.davidson@umconnect.umt.eduOffice 	Hours: Wed 2:00-4:30	 - meet in SS 402 Course	 Objectives	The	purpose	of	this	course	is	to	introduce	you	to	essential 	data 	structures	and	 algorithms that will serve as valuable building blocks for the remainder of your career	as	a computer scientist. In this class, we emphasize fundamental understanding andimplementation. You will: •	 Become familiar with fundamental data structures like stacks, queues, priorityqueues,	associative	arrays	/	hashes,	and	graphs	(e.g.	 search	 trees	 and	 perhaps	tries) •	 Become familiar with fundamental algorithms based on these data structures,including	sorting,	clustering, graph	search, and 	string	search  	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 			 		 			 	 	 	 	 	 	 	 		 	 				 			 	 	 	 	 		 	 		 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 		 						 				 	 			 	 		 	 			 	 	 	 	 	 		 		 	 	 	 	 			 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 		 			•	 Improve your facility with software development, by implementing these datastructures	 and	 algorithms in Java •	 Become familiar with the basic notion of run time and 	space analysis,	as 	applied to algorithm	 development Other	 courses	 required	Prerequisite:	 CSCI 136Corequisite:	 M	225 	or 	M	307 (in prep for Analysis of Algorithms, CSCI 332) Required	textbook	 Algorithms Fourth EditionBy	 Robert	Sedgewick	and	Kevin	WayneBooksite: http://algs4.cs.princeton.edu Schedule		 Below	is an	ordered 	set	of 	topics I	expect	to cover.	 It	is subject to	 change.	 Please	 consultmoodle for	 up-to-date	 schedule and 	reading assignments.	Lectures	will	cover the	 reading material as comprehensively as possible. Students are expected to supplementlectures 	with 	a	careful	study 	of 	the 	relevant	sections 	of 	the 	textbook. •	 Fundamentals (Objects, data types, APIs, Analysis,	 Stacks	and Queues) •	 Sorting	(Elementary, Mergesort, Quicksort, Priority Queues) •	 Searching	(Symbol Tables,	Search	trees,	Hash	tables)	 •	 Graphs	(Directed	and	undirected,	Spanning	trees,	Shortest 	paths) •	 Strings 	(Tries,	Substring	search) Assignments will include both problem	 sets (e.g. questions from	 the text) andprogramming assignments. Grading		 	Programming: 35% Homework: 25% Exams: 30% Quizzes: 10% The	“curve”:		you	 may have	heard	that 	the	 grades	 assigned on my exams and homework are 	often	quite 	low.	I	 account	for this,	and 	set	grade 	cutoffs accordingly.	 Cutoffs 	are	 usually	 lower 	than	the	typical 	90/80/70	splits.	I	will	provide	an	update	with	approximate cutoffs as the semester progresses. Late	policy	 Submissions for programming and homework assignments are due	 at the	 beginning	 of	class. Late submissions will not be accepted. Every student will get one free extension onan assignment (programming or homework) for up to a week. You do not have to ask forthis – just	write	that	you	are	using	your 	free	extension	when	you	turn	it	in.	Don’t	waste	 this 	extension	or 	feel	obligated to 	use 	it; 	another 	extension	will	be 	given	only 	in	 exceptional circumstances. 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 					 				 				 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 				 	 	 	 	 	 	 	 	 	 	 	 	 	 	Flipped 	classroom 		In a flipped classroom, you watch video lectures online before class and the class time is	spent on short presentations, discussions, individual and group problem	 solving. Thepurpose	of this 	approach	is 	to	turn	the	“talking-head” 	part 	of	instruction	(i.e.	the	lectures) into something you can do at home, and make our time in class into a more	engaged	experience.	 This	approach	places	a 	clear	burden	on	you:	 •	 Before class, watch the recorded lectures and skim	 the assigned pages in thebook.	 I will assume that you have done so. It will be apparent if	you	arrive	in	class	without 	having	watched	the assigned lectures. Why? Because class time will	be 	spent	with 	activities 	such 	as: •	 Talking with classmates (and	instructors) about the day’s material •	 Working on problems in small groups •	 Presenting	the	 day’s material to others in the class •	 Answering questions from, and building on concepts presented in, thelectures •	 After class •	 Review any of the lecture videos that remain confusing •	 Read	the	text	in	detail Sedgewick	lectures,	part	1:		 http://bit.ly/2xJSbVm (youtube) Sedgewick	lectures,	part	2:		 http://bit.ly/2vZFkMy (youtube) Cheating	Academic dishonesty (including plagiarism	 and cheating) will not be tolerated. (Un-caught)	cheating	hurts	all 	involved: •	 It	devalues 	the	grades 	earned by others in the class, and the degree from	 our program •	 It leaves you without the skills you’ve asked (and paid) me to help you gain •	 (Actually, it doesn’t hurt me) Consult the university’s student conduct code for more details. I will follow the guidelines given there. I will seek out the maximum	 allowable penalty for any academicdishonesty 	that	occurs 	in	this 	course.	If 	you	have 	questions 	about	what	constitutes acceptable use of resources, please feel free to reach out to me – I’ll	respect	yourattempts to understand the ethics of using others work. Specifically, do not search for answers online. I’m	 not naïve enough to think these don’t exist,	and	I	have	caught 	>20	 people plagiarizing from	 the web in the past three years.	 Most	have 	received 	a	failing	grade. This is not an idle threat: a graduate student working with me has developed software 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 					 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 				 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 		 	 	 	 		 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 		 	 	 	 	 		 				 	 	 	 	 	 	 	 	 	 	 		 	 		 	 	 	 	 	 	 	 	 	for identifying online plagiarism, and we will apply that software to your submissions. I	retain the right to question you about the material turned in. If it is	evident 	that you	don’t understand what you turned in, I may view your submission as an instance ofcheating.	 Working	 in 	groups	 (homework)	You	are 	encouraged to 	work	together in small groups - the 	best	way to 	understand 	the subtleties of the homework problems is to argue about the answers. Each of you shouldlook	at	 (and	work 	on)	 all the problems independently, and not just sub-divide	 the	questions among group member. After discussing problems and coming up withsolutions,	 you	will 	each	 write 	up	a	separate submission. Do	 not write	 your	 solutions	 upthen share them	 with someone else. Though the ideas behind your solutions may besimilar, the text should be 	your 	own	 – demonstrate your command of the problem	 with a personalized 	solution. (Don’t 	be	a 	leech and 	let	your 	partner 	do 	all	the 	work.	Unless 	you	learn	how	to 	solve problems, you will get burned on the exams and thus for your final grade. You	also 	won’t	 learn the material, which will harm	 you in future classes and employment)	 Working	 in 	groups	 (programming	 assignments)	I encourage discussion with others regarding programming assignments, as well. Aswith homework, these should be high-level	discussions.	Code 	should be 	written	 independently. If I suspect copying or plagiarism, I will ask you to explain	each	piece	of	the code to me, possibly resulting in a reduced grade or removal from	 class. Attendance 	Attendance is required. You are responsible for all material presented in class; some ofthat material is not covered in the textbook. Computers	You	 may develop your programs on any machine that you like: we encourage you to useyour own equipment. In	the	first	lab,	we	will provide	instructions 	for 	setting	up	a	Java	 programming environment under Windows, Mac 	OS	X,	and Linux. Laptops	 will be	 available 	during	lab	sections. Disabilities 	Students with disabilities are encouraged to meet with me to discuss anyaccommodations they require. Electronic	 devices	 Turn	off	your	cellphone,	or	set it 	to	vibrate	during	class.	Take	calls	outside	the	classroom. Students texting during class will be asked to leave. 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 		Personal 	contact 	I hope to establish as much personal	contact	with 	each 	of 	you	as 	is 	possible 	in	a	class 	this size.	 Don’t be	 afraid	 to	 visit my office	hours, or stop by my office to 	ask	questions 	or 	say hello.	 In	fact: 	I	require	that	you	do	so	at	least	once.		Details 	will	be	given	in	class. 

CSCI 232.00: Data Structures and Algorithms

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CSCI 232.00: Data Structures and Algorithms

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