CSE 205 Wi 2012 – Intro & Chapter 1

CSE 205 Wi 2012 – Intro & Chapter 1

Acknowledgments 1.  Prof. P. Sadayappan, CSE 2.  Prof. D. Kaplan (Author of Text)

Course Objectives •  Skills for Computational thinking. •  Master basic constructs provided by high-level imperative programming languages •  Be knowledgeable of concepts including sequencing, selection, and iteration •  Gain exposure to algorithmic thinking •  Be aware of computational approaches to solving problems •  Be familiar with basic data structures such as arrays and procedural composition •  Gain exposure to computational methods of simulation/optimization/data analysis. •  Be knowledgeable of computational complexity and performance issues.

Course Logistics •  Instructor:

–  Raghu Machiraju, –  Office: DL779 –  Email: [email protected] –  Phone: 1-614-292-6730

•  Class –  Lecture - TR: 09:00AM-10:18AM DL0305 –  OpenLab - F: 08:30AM-09:18AM BE

•  Instructor Office Hours@DL779 –  TR: 8 - 8:45 AM, 10:30-11:00 AM –  F: 9:30-10:00 AM

Logistics(2) •  Grader

–  Do Young Park –  Office: DLXXX –  Email: [email protected] –  Phone: 1-614-XXX-YYY

•  Grader Office Hours@DLxx TBA

Logisitics(3) •  Final Exam

–  Mon, March 12, 7:30 AM - 9:18 AM

•  Course Webpage with all details: http://www.cse.ohio-state.edu/~raghu/teaching/cse205wi2012

•  Content will be incrementally posted on Carmen

Course Grading

•  Assignments: 50% (10 x 5)

•  Midterm: 20%

•  Final Exam: 30%

•  Home works : Posted on most Thursdays –  Due by next Wednesday midnight- Submit

electronic copy on Carmen.

– Submit hard copy next Thursday class.

Course Grading

What is Computation?

Chapter 1: Outline – 1.1 Computation as a transformation

– 1.2 Computation as a reaction to events

– “State” and change of “state”

– 1.3 Algorithms

Reading Assignment: Chapter 1

1.1 Computation as Transformation

– Input = x; Output =√x

http://www.macalester.edu/~kaplan/scientificprogramming/figures.ppt

–  Input = financial info; Output = tax owed OR refund


Example: Computation as Transformation

– Input = image file; Output = “This is the Mona Lisa!”




EE

OH

Speech sounds as recorded by a microphone.

1.2 Computation as Reaction to Events

•  Inputs often come from measurements •  Measurements are typically performed

using transducers (convert input signal into numbers) – microphone – speedometer – thermometer – seismograph – radar

Example: Computation as Reaction

http://research.utk.edu/ora/explore/sundial.jpg

– Sundial (and compass): Analog transducers

The operation of sixteenth-century sundial. By aligning the sight holes with the sun, the string transduces the altitude of the sun onto a set of several scales that indicate the time of day. The appropriate scale is picked based on the day of the year.

Example: Computation as Reaction

A transducer from temperature to position

– Because measurements (signals) typically change over time, we get the concept of state : the set of values describing a system at a given time

– E.g. • Health: <blood pressure, temperature, heart rate> • Airplane: <heading, altitude, fuel> •  Subatomic particle: <position, momentum>

– An important model of computation • A computation as a transition from an old

state to a new state.

Computation as Reaction to Events

A Turing Machine (abstract)

A Computer

1.3 Algorithm ●  A step-by-step specification of “basic” transformations

●  Recipe analogy: How to make a cake? 1.  Mix ½ cup lard, ½ cup butter, getting them good and creamy. 2.  Mix in 1 cup flour, ¾ cup brown sugar, ¾ cup white sugar, 2 eggs, 1 tsp. vanilla, and ½ tsp. baking soda. 3.  Work this over until it is thoroughly mixed, then blend in 1 ¼ cup more flour. 4.  Bake at 375 degrees 8-10 minutes.

•  Illustrates three key aspects of algorithms: •  Sequencing of steps •  Repetition •  Conditional execution (perform if some test condition holds)

Multi-digit Addition Several snapshots of the

paper-and-pencil algorithm for adding two multidigit numbers.

Example of Algorithm: Compare Numbers

•  Compare two Arabic numerals (e.g., 23 and 97) to determine which is larger 1.  Count the digits in each numeral. If one has

more digits than another, the one with more is the larger.

2.  Otherwise, compare the leftmost digits in the two numerals. If one digit is larger than the other, the numeral with the larger leftmost digit is the larger numeral.

3.  Otherwise, chop off the first numeral and go to step 2.

Example: Addition of 3 Numbers

Flow Charts

?

Example: Find Smallest

•  Example: Find smallest number in list 1.  Assume smallest is first item in list. 2.  If there are no more items in list, we're

done. Report smallest number. 3.  Otherwise, look at next item. If it's smaller

than current assumed “smallest” value, make current answer equal to value of this item.

4.  Go to step 2.

Example: Find Smallest

LIST: 5 2 9 1 3

LIST: 5 2 9 1 3

SMALLEST-SO-FAR: 5

LIST: 5 2 9 1 3

SMALLEST-SO-FAR: 2

LIST: 5 2 9 1 3

SMALLEST-SO-FAR: 2

LIST: 5 2 9 1 3

SMALLEST-SO-FAR: 1

LIST: 5 2 9 1 3

SMALLEST-SO-FAR: 1

LIST: 5 2 9 1 3

SMALLEST: 1

Algorithm: Find Smallest

The Flow Chart ?

Stepping Through Algorithm

Advanced Example: Sorting a List

•  Example: Sort a list of numbers 1.  Create a new, empty list 2.  Find the smallest item in the old list. 3.  Remove this item from the old list and add it

to the beginning of the new list. 4.  If the old list is empty, we're done. Output

the new list. 5.  Otherwise, go to step 2.

Example: Sorting a List OLD: 5 2 9 1 3 NEW:

NEW: OLD: 5 2 9 1 3

OLD: 5 2 9 3 NEW: 1 OLD: 5 2 9 3 NEW: 1

OLD: 5 9 3 NEW: 1 2

OLD: 5 9 3 NEW: 1 2

OLD: 5 9 NEW: 1 2 3

OLD: 5 9 NEW: 1 2 3 OLD: 9 NEW: 1 2 3 5

OLD: NEW: 1 2 3 5 9

Algorithm: Sorting a List

The Flow Chart ?

Stepping Through Algorithm

What is next ?

•  Build Software ? •  Which platform ? •  Let us name some …

Algorithms to software

•  Solving equations •  Searching through data for matches •  Finding maxima and minima •  Simulating random processes

Elements of Software

•  Representations – Lists (Searching, Sorting) – Object Assemblies (Bridge)

•  Modules and Modularity •  Visualization •  Enforce engineering discipline

Visualization

Nomograms

Nomogram (2)

More Visualization

Visualization(2)

Visualization(3)

Recap

CSE 205 Wi 2012 – Intro & Chapter 1

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