• Aneta Alexandridis, grad student, ECE dept. (9 AM) (seas15@seas.gwu.edu)

 
• Jennifer Sri, senior, CS dept. (12 Noon) (cs411@seas.gwu.edu)

 
• Philip Wang, senior, CS dept. (2 PM) (cs412@seas.gwu.edu)

“Computer science is a discipline that involves the understanding and design of computers and computational processes. In its most general form it is concerned with the understanding of information transfer and transformation.

“A central focus is on processes for handling and manipulating information. Thus, the discipline spans both advancing the fundamental understanding of algorithms and information processes in general as well as the practical design of efficient reliable software and hardware to meet given specifications.”

source: Computing Sciences Accreditation Board (CSAB), “Defining the Computing Sciences Professions”, http://www.csab.org/comp_sci_profession.html

“A well educated computer scientist should be able to apply the fundamental concepts and techniques of computation, algorithms, and computer design to a specific design problem.

“The work includes detailing of specifications, analysis of the problem, and provides a design that functions as desired, has satisfactory performance, is reliable and maintainable, and meets desired cost criteria.

“Clearly, the computer scientist must not only have sufficient training in the computer science areas to be able to accomplish such tasks, but must also have a firm understanding in areas of mathematics and science, as well as a broad education in liberal studies to provide a basis for understanding the societal implications of the work being performed.”

source: ibid.

• Computing technologies are constantly changing.

 
• Some things are really new, others are just “new”— just marketing hype, or a small evolutionary step sold as a revolutionary breakthrough.

 
• A professional computer scientist must be prepared to learn the “new” languages, technologies, computers, etc., and to be able to distinguish the truly new from the “new”.

 
• A good curriculum will expose you to a number of theories, systems, languages, technologies, etc., so you can learn how to learn these, and to compare them.



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In GW terminology,
 

• a minor is always in the same school (SEAS, CSAS, SBPM, ESIA) as your major school
• a secondary field is in a different school from your major school
• in a given field, the curriculum requirements for a minor are not necessarily the same as for a secondary field.

 

Intro to Computer Science (41)
Intro to Software Development (51)
Algorithms and Data Structures I (131)
Software Engineering I (141)

(plus Math 21 or 31 and 2 CSci electives)

• Historically, GW has made it nearly impossible to take two majors, each in a different school, in four years. (It's been easier in five.)
• However, this is changing rapidly. All the schools seem to be getting ready to allow two majors across two schools.
• Obviously a dual-major student will have to fulfill the requirements of both majors (many courses can be "double-counted").

 
• The issue rarely came up in SEAS—SEAS programs historically have had very few "empty" course slots, in which to fill in courses from another major.
• However, it is academically possible to combine another major with the BA in Computer Science—there are enough "slots."
• SEAS and ESIA have agreed on a dual-major program; we are also working actively on this with CSAS and SBPM.

Registering for Courses as a CS Undergrad

The registration process is:
 

1. Complete a SEAS Undergraduate Advising Form with your course selections.
2. Visit your advisor (currently Prof. Feldman or Prof. Meltzer) to discuss your program and obtain an advisor' signature on the form.
3. Take the form to the SEAS Student Records Office, Tompkins 103. They will remove the advising hold.
4. Complete your registration by web or phone.

It is in everyone's interest for you to register each semester during the early registration period. Do not wait till the next semester!

• 1965: Gets "turned on" to computers in a junior-year electrical engineering course. Uses IBM-7094—$6,000,000, 1MHz, 128k RAM. Gets summer job working for IBM sales. Decides sales is not his cup of tea.
• 1966: Starts graduate work in Computer Science. Gets summer job writing programs to test half-built computers.
• 1970: Gets full-time job programming for Educational Testing Service.
• 1975: Joins faculty at GW. Teaches CSci 51 and 131 using mainframe computer with punched-card input. Students get one run a day (if they are lucky).
• 1980: Buys his first home computer: Commodore Vic-20, $300., 1MHz, 8k RAM. Uses TV set for display, cassette recorder for input.
• 1982: Buys Commodore-64 computer. $300.00. Similar to Vic-20, but has 64k RAM and faster processor (2 MHz).
• 1984: Buys "fat" Apple Macintosh: 7 MHz, 512k RAM. 9" B/W display, Window/Icon/Menu/Pointer (WIMP) interface. $2500.00.
• 1987: Buys Macintosh Plus. 1Mb RAM, 8 MHz, $1800.
• 1991: Buys Macintosh IIci, 16 Mb RAM, 25 MHz, separate monitor, $2500.
• 1996: Buys Power Macintosh 7500. Upgrades progressively with bigger hard drives, more memory. Now has 200 MHz CPU, 128Mb RAM, 12 gb disks. Total (including upgrades) about $3000.
• 2000: Now owns a Mac and a Pentium/Windows/Linux box, Mac laptop, Dell laptop, etc. If anyone had predicted in 1965 that I'd own all this stuff, I'd have said they were crazy.

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source: The Analytical Engine, p. 26

QUESTION: Suppose a program is designed not to halt in a finite amount of time? Is it implementing an algorithm?

"The British mathematician Alan Turing proved [1936] that an algorithmic approach can be used to solve any mathematical or logical problem for which a solution is known to exist. This means that one can deduce that any solvable problem can be handled by a computer, provided the correct algorithm is used.

"In computing, an algorithm is the plan, routine, or process of defining a set of instructions so that a computer program can be written to find the answer / solution to a given problem or task."

source: Prentice-Hall's Illustrated Dictionary of Computing (3rd ed.) (Prentice-Hall, 1998), p. 19

Problem Specification. You are driving a car with two friends and suddenly get a flat tire. Fortunately, there is a spare tire and jack in the trunk.

Analysis. After pulling over to the side of the road, you might decide to subdivide the problem of changing a tire into the subproblems below.

Design. Here are the main steps in an algorithm to change a tire.

Algorithm.
1. Loosen the lug nuts on the flat tire; don’t remove them yet.
2. Get the jack and jack up the car.
3. Remove the lug nuts from the flat tire and remove the tire.
4. Get the spare tire, place it on the wheel, and tighten the nuts.
5. Lower the car.
6. Secure the jack and flat tire in the trunk.

Because these steps are relatively independent, you might decide to assign subproblem 1 to friend A, subproblem 2 to friend B, subproblem 3 to yourself, and so on. If friend B has used a jack before, the whole process should proceed smoothly; however, if friend B does not know how to use a jack, you need to refine step 2 further.

Step 2 Refinement
2.1. Get the jack from the trunk.
2.2. Place the jack under the car near the flat tire.
2.3. Insert the jack handle in the jack.
2.4. Place a block of wood under the car to keep it from rolling.
2.5. Jack up the car until there is enough room for the spare tire.

Step 2.4 requires a bit of decision making on your friend’s part. Because the actual placement of the block of wood depends on whether the car is facing uphill or downhill, friend B needs to refine step 2.4.

Step 2.4 Refinement

2.4.1 If the car is facing uphill, then place the block of wood in back of a tire that is not flat; if the car is facing downhill, then place the block of wood in front of a tire that is not flat.

This is actually a conditional action: One of two alternative actions is executed, depending on a certain condition.

2.4.1 If the car is facing uphill, then place the block of wood in back of a tire that is not flat.

2.4.2 If the car is facing downhill, then place the block of wood in front of a tire that is not flat.

QUESTION: What if the car is on flat ground?

Finally, step 2.5 involves a repetitive action: moving the jack handle until there is sufficient room to put on the spare tire. Often, people stop when the car is high enough to remove the flat tire, forgetting that an inflated tire requires more room. It may take a few attempts to complete step 2.5.

Step 2.5 Refinement.

2.5.1. Move the jack handle repeatedly until the car is high enough off the ground that the spare tire can be put on the wheel.

• (2000 B.C.) abacus
• (1642) Blaise Pascals's mechanical adding machine (France)
• (1842) Charles Babbage's Analytical Engine (England)
• (1890) Herman Hollerith's punched-card machines (U.S., D.C.)
• (1939) Alan Turing's vacuum-tube Colossus (England)
• (1939) John Atanasoff's digital computer (U.S., Iowa)
• (1940) Konrad Zuse's relay calculator (Germany)
• (1943) Howard Aiken's Mark I (Harvard, electromechanical Analytical Engine)
• (1944) Eckert and Mauchly's ENIAC (Univ. of Penna.)
• (1946) John von Neumann's stored-program computer—stores instructions and data in same RAM (Princeton)
• (1951) Eckert and Mauchly build first general-purpose commercial computer, UNIVAC (Philadelphia)

Code Symbolic form 16 ADD #670 ACC + 670 -> ACC 17 SUB #235  ACC - 235 -> ACC 18  MUL #160 ACC - 160 -> ACC 19 DIV #160 ACC / 160 -> ACC 20 LOD #265 number 265 -> ACC 4 LOD P contents of loc. P -> ACC 5 STO Y ACC -> loc. Y 0 ADD T ACC + contents of loc. T -> ACC 1 SUB W ACC + contents of loc. W -> ACC 2 MUL D ACC * contents of loc. D-> ACC 3 DIV Q ACC / contents of loc. Q -> ACC 12 JMP R Get next instruction from loc. R 13 JMZ S If ACC = 0, go to loc. S,  otherwise go to next instruction 14 NOP Do nothing; just go to next instruction 15 HLT Halt execution; do nothing more