Instructor: Ivan Avramovic
Email: iavramo2-at-gmu.edu
Hours: 1:30-2:30pm Mon/10:30-11:30am Tue, 4609 Engineering Building

Assistants:
Pushkal Reddy (preddy6-at-gmu.edu); GTA/grader
William Duggleby (wduggleb-at-gmu.edu); UTA

Prerequisites: none
Required textbooks:
W. Daniel Hillis, The Pattern on the Stone: The Simple Ideas that Make Computers Work ;
Wentworth, Elkner, Allen, and Meyers, How to Think Like a Computer Scientist: Learning with Python 3 ; (available free online at http://openbookproject.net/thinkcs/python/english3e/)

Webpage: https://cs.gmu.edu/~iavramo2/classes/cs100f19.html
Piazza: https://piazza.com/ for questions and discussion
Blackboard: https://blackboard.gmu.edu/ for assignments and grades
Schedule: see below
Computing requirements: students will need a computer on which they can access Internet sites and install programs, especially Python. There are computer labs on campus which provide this capability, but students may find it preferable to use their own computers.

Description

This course is intended to help students learn to think in the manner necessary to fully grasp the nature and power of the digital world around us. The early era of the Internet and the personal computer led to the need for "computer literacy." Now the changing nature of our global society requires that students learn new ways to think about problems and how to solve them, regardless of students' specific fields of endeavor. Through this course, students will explore major issues related to the "big ideas" of computational thinking (namely, Creativity, Abstraction, Data, Algorithms, Programming, Internet, and Societal Impact), as well as how these issues will impact their future lives.

Outcomes

1. Students will be able to use technology to locate, access, evaluate, and use information, and appropriately cite resources from digital/electronic media.
2. Students will understand the core IT concepts in a range of current and emerging technologies and learn to apply appropriate technologies to a range of tasks.
3. Students will understand many of the key ethical, legal, and social issues related to information technology and how to interpret and comply with ethical principles, laws, regulations, and institutional policies.
4. Students will understand the essential issues related to information security, how to take precautions and use techniques and tools to defend against computer crimes.
5. Students will recognize the significance of the "big ideas" of computational thinking.

Topics

• Representation of ideas with bits
• Basic boolean logic
• Devices to implement logic functions
• Programming languages for data and action
• Primitive operations and abstraction
• Algorithms for work and play
• Communication between machines
• Computing security concepts
• Basic data analysis
• Impact of automation and communication on human societies
• Human and machine intelligence

Grades

Material	Weight	Amount	Notes
Homework assignments	40%	7
In-class assignments	10%	5-7	not announced in advance
Mini exams	30%	4	30 minutes in-class
Final exam	20%	1	comprehensive

Grading Scale

Grade	A+	A	A-	B+	B	B-	C+	C	C-	D	F
max	↑	97	91	89	87	81	79	77	71	69	59
min	98	92	90	88	82	80	78	72	70	60	↓

Grading Policies

• Homework is due electronically (via Blackboard) before the start of class on the day it is due. Unlimited resubmissions within the deadline are allowed, but only the most recent is graded.
• For grade disputes please contact the grader (the TA in most cases) first. You must initiate a dispute within a week of receiving your grade for the dispute to be considered.
• Late work is penalized -10% per day late, up to a maxiumum of 4 days. Thus, an assignment submitted 2 days late which would have scored an 87 would instead receive a 67. An assignment submitted during class on the day it is due is considered one day late. An assignment which is more than 4 days late will not be accepted.
• You may be required to show your Mason ID for exams. A missed exam will result in a zero.
• Please contact the instructor as soon as possible if there are extenuating circumstances which affect your ability to complete an assignment or exam.

Honor Code

Any direct contribution from others on an assignment or exam, except where expressly permitted, will be treated as a violation of George Mason's Honor Code. If someone is caught cheating, they will be referred in an honor code case, which will most likely result in failing the course. Cheating by copying is often remarkably easy to detect by electronic means, so please do not put yourself at risk by cheating.

To provide an example of what cannot be done, the following are considered cheating and are not permitted:

• Directly using another student's solution to a problem, even if it is from a different semester.
• Asking a friend for a solution or paying someone for a solution.
• Searching for a solution in an outside source such as the Internet or a solution manual.
• Pasting your solutions to a public or semi-public site, including the message forum for this course (except in a private posts to the TAs).
• Collaborating or copying during exams.
• Assisting others in any of the above.

By constrast, the following is considered fair game during this course:

• Discussing the homework with other students in the class via informal, non-specific approaches to how a problem can be solved (do not share code, however).
• Getting help from classmates in solving a specific type of bug in a program (as long as it doesn't lead to discussing how the code should be structured).
• Searching on the Internet for alternate explanations of concepts discussed in class.

When learning to program, the ability to discuss with others is important to gain a footing and to better understand what is going on. At the same time, solving problems individually helps us better prepare for situation in which there are no other resources to rely on (like exams). If you are unsure whether something is fair game or cheating, ask an instructor. A good rule of thumb is that when you submit something, especially a piece of computer code, you should understand it well enough to be able to explain how it works.

Suggestions and additional policies

• Make backups of all of your work. We cannot grade what you do not submit, so guard against the possibility that something happens to your computer or your assignment by saving your work someplace else. Consider using a cloud backup such as Dropbox. Accidentally deleting your work is not grounds for an extension.
• Make sure that your code at least does something. If your code cannot compile or run due to errors, it is likely to receive little to no credit.
• Make sure to keep at least one untouched version of each assignment you submit. If there are issues with the submission, you will only receive credit if we can verify that it has not been modified since you submitted it.
• It is recommended that you complete each reading assignment before the class where it is shown, to be able to better follow the material.

Schedule

Week	Date	Topic	Reading	Due
week 1	Aug 26	Overview	Pattern chapter 0
	Aug 28	Primitive operations; Algorithms	Pattern chapter 1
week 2	Sep 2	(no class: Labor Day)
	Sep 4	Bits and bytes; Bit interpretation
week 3	Sep 9	Boolean logic	Pattern chapter 2	HW 1
	Sep 11	Python; Turtle graphics	Pattern chapter 3; Think chapters 1-3
week 4	Sep 16	Review; Mini-exam 1		HW 2
	Sep 18	Abstraction using functions	Think chapters 4-5
week 5	Sep 23	Programming loops and conditionals	Think chapters 6-7
	Sep 25	Computability	Pattern chapter 4
week 6	Sep 30	Python lists	Think chapter 11	HW 3
	Oct 2	Pyton lists, cont.
week 7	Oct 7	Review; Mini-exam 2
	Oct 9	Algorithmic complexity	Pattern chapter 5
week 8	Oct 15	Search algorithms; (Monday classes meet Tuesday)		HW 4
	Oct 16	Parallel computing	Pattern chapter 6
week 9	Oct 21	Encryption	Pattern chapter 7
	Oct 23	Review; Mini-exam 3
week 10	Oct 28	Operating systems		HW 5
	Oct 30	The Internet; HTML
week 11	Nov 4	Putting stuff on the Web
	Nov 6	Simulation; Randomness	Pattern chapter 8; ML Tutorial
week 12	Nov 11	Artificial Intelligence; Machine Learning	Plan for SPAM, Frontline videos	HW 6
	Nov 13	Privacy with machine learning
week 13	Nov 18	Automation	Pattern chapter 9	HW 7
	Nov 20	Intellectual property
week 14	Nov 25	Review; Mini-exam 4
	Nov 27	(no class: Thanksgiving)
week 15	Dec 2	Putting it all together
	Dec 4	Review
--	Dec 16	Final Exam, 10:30am–1:15pm