What is Computer Science About? Part 1: Computational Thinking

1. What is Computer Science About?Part 1: Computational Thinking

2. Main Points • There is more to Computer Science than just programming. • Computer Science is about computational thinking and algorithms. • We try to formalize activities into repeatable procedures and concrete decisions. • Generalizing a procedure into an abstract algorithm helps us recognize if there are known solutions, and how complex the problem is. • Programming is just translating an algorithm into a specific syntax.

3. Some definitions... • Computational thinking • translating processes/procedures into step-by-step activities with well-defined choice points and decision criteria • Design and analysis of algorithms • expression of a procedure in terms of operations on abstract data structures like graphs, lists, strings, and trees • finite number of steps (clear termination conditions; it has to halt) • is the algorithm correct? • are all cases handled, or might it fail on certain inputs? • how much time will it take? how much space (memory)? • Programming • translating algorithms into a specific language • Software engineering • managing the development and life-cycle of a system, including design/specification, documentation, testing, use of components/libraries, release of new/updated versions • usually a team effort

4. Computational Thinking • CT has infused into all kinds of fields from cooking and sports, to transportation, medical diagnosis, and particle physics • Many intelligent activities are often ill-defined, and CT is about formalizing them into concrete decisions and repeatable procedures • Think about how to find a good place to eat in a new town • ask a friend? desired type of food? consult Zagat’s? look for restaurant with many cars in the parking lot? • Think about how choose a book to read • interest? availability? recommendations? reviews? • “Finding Waldo” (how do you search for shapes in images?)

5. Google’s ideas on Computational Thinking http://www.google.com/edu/computational-thinking/what-is-ct.html

6. Mechanical analogies • Think about how a thermostat does temperature control

7. Mechanical analogies • Think about how a thermostat does temperature control • What are the actions that can be taken? • What are the conditions under which these actions are triggered? • What parameters affect these decisions?

8. Mechanical analogies • Think about how a thermostat does temperature control • What are the actions that can be taken? • What are the conditions under which these actions are triggered? • What parameters affect these decisions? Let T be the desired or “control” temperature if temp>T, turn on AirConditioner if temp<T, turn on Heater

9. Mechanical analogies • Think about how a thermostat does temperature control • What are the actions that can be taken? • What are the conditions under which these actions are triggered? • What parameters affect these decisions? Let D be the acceptable range of temp. variation if temp>T+D, turn on AirConditioner if temp<T-D, turn on Heater

10. Mechanical analogies • Think about how a thermostat does temperature control • What are the actions that can be taken? • What are the conditions under which these actions are triggered? • What parameters affect these decisions? if temp>T+D, turn on AirConditioner if temp<T-D, turn on Heater if AC on and temp<T, turn AC off if HE on and temp>T, turn HE off

11. Mechanical analogies • Think about how a thermostat does temperature control • What are the actions that can be taken? • What are the conditions under which these actions are triggered? • What parameters affect these decisions? • Think about how a soda machine works • Keep accepting coins till enough for item • Dispense item (if avail.), then make change • Think about the decision policy for an elevator • Think about the pattern of traffic signals at an intersection (with sensors) • How do lights depend on cars waiting? Pedestrians?

12. Ultimately, we formalize these things into: • flowcharts and pseudocode • abstractions like finite-state machines procedure bubbleSort( list A ) repeat swapped = false for i = 1 to length(A)-1 do: if A[i] > A[i+1] then swap( A[i], A[i+1] ) swapped = true until not swapped 1 3 7 9 2 11 13 17 1 3 7 2 9 11 13 17 These play a big role in compilers, network protocols, etc. Finite-state machine representing a turnstile

13. The “Earliest” Known Algorithm • Euclid’s algorithm for determining the GCD (greatest common denominator) – also known as the Chinese Remainder Theorem • Problem: given two integers m and n, find the largest integer d that divides each of them • Example: 4 divides 112 and 40; is it the GCD? (no, 8 is)

14. Euclid’s algorithm: repeatedly divide the smaller into the larger number and replace with the remainder • Questions a Computer Scientist would ask: • Does it halt? (note how a always shrinks with each pass). • Is it correct? • Is there a more efficient way to do it (that uses fewer steps)? GCD(a,b): if a<b, swap a and b while b>0: let r be the remainder of a/b a←b, b←r return a • a=112, b=40, a/b=2 with rem. 32 • a=40, b=32, a/b=1 with rem. 8 • a=32, b=8, a/b=4 with rem. 0 • a=8, b=0, return 8

15. ...UCLA 92 Stanford 80 – OklaSt 55 Iowa 61 – Indiana 83 MichSt 82 ... • While monitoring a stream of basketball scores, keep track of the 3 highest scores • Impractical to just save them all and sort • How would you do it?

16. A B C ...UCLA 92 Stanford 80 – OklaSt 55 Iowa 61 – Indiana 83 MichSt 82 ... • Algorithm design often starts with representation • Imagine keeping 3 slots, for the highest scores seen so far • Define the “semantics” or an “invariant” to maintain: • A > B > C > all other scores

17. A B C p A B A p B A B p ...UCLA 92 Stanford 80 – OklaSt 55 Iowa 61 – Indiana 83 MichSt 82 ... • Algorithm design often starts with representation • Imagine keeping 3 slots, for the highest scores seen so far • Define the “semantics” or an “invariant” to maintain: • A > B > C > all other scores • With each new game score (p,q) (e.g. Aggies 118, Longhorns 90) if p>A then C=B, B=A, A=p else if p>B, then C=B, B=p else if p>C, then C=p repeat this “shifting” with q

18. A B C p A B A p B A B p ...UCLA 92 Stanford 80 – OklaSt 55 Iowa 61 – Indiana 83 MichSt 82 ... • Algorithm design often starts with representation • Imagine keeping 3 slots, for the highest scores seen so far • Define the “semantics” or an “invariant” to maintain: • A > B > C > all other scores • With each new game score (p,q) (e.g. Aggies 118, Longhorns 90) if p>A then C=B, B=A, A=p else if p>B, then C=B, B=p else if p>C, then C=p repeat this “shifting” with q • Questions to consider: • What happens initially before A, B, and C are defined? • What happens with ties? • Should A, B, and C represent distinct games, or could 2 of them come from the same game?

19. Spell-checking • Given a document as a list of words, wi, identify misspelled words and suggest corrections • Simple approach: use a dictionary • for each wi, scan dictionary in sorted order • Can you do it faster? (doc size N x dict size D) • Suppose we sort both lists • Sorting algs usually take N log2 N time • Example: if doc has ~10,000 words, sort in ~132,000 steps • Assume you can call a sort sub-routine (reuse of code) • Note: you will learn about different sorting algorithms (and related data structures like trees and hash tables) and analyze their computational efficiency in CSCE 221 • Can scan both lists in parallel (takes D steps) • (D + N log N < ND)

20. Words in a document like the US Declaration of Independence: abdicated abolish abolishing absolute absolved abuses accommodation accordingly accustomed acquiesce act acts administration affected after against ages all allegiance alliances alone ... Words in the English Dictionary: ... achieve achromatic acid acidic acknowledge acorn acoustic acquaint acquaintance acquiesce acquiescent acquire acquisition acquisitive acquit acquittal acquitting acre acreage acrid acrimonious ... note that this list is “denser”

21. occupashun ||||||***| occupation occupashun ||||****** occurrence occupashun |||***|**| occl-usion d=3 d=6 • A harder problem: suggesting spelling corrections • Requires defining “closest match” • Fewest different letters? same length? • occupashun  occupation • occurence  occurrence • “Edit distance”: • Formal def: # diff letters + # gap spaces • Minimal dist over all possible gap placements calculated by Dynamic Programming • How to efficiently find all words in dictionary with minimal distance? • Does context matter? • Used as noun or verb (affect vs. effect) • Compliment vs. complement • Principle vs. principal d=5

22. Summary about Computational Thinking • CT is about transforming (often ill-defined) activities into concrete, well-defined procedures. • A finite sequence of steps anybody could follow, with well-defined decision criteria and termination conditions • Take-home message: the following components are important to computational thinking: • Decomposition • Identifying patterns and repetition • Abstraction and generalization • Choosing a representation for the data • Defining all decision criteria