1. Intro to CSCI-130 Computing: Science & Applications (NS)

2. Layered Architecture LAYEROrder Application SW : Excel & Access 2 High-order P.L. : Visual Basic 1 Low-order P.L. : Assembly 3 System Software : O.S. 3 Machine Language 4 Data Representation 5 HW: Circuit Design6

3. General vs. Special Computers  Computers can either be  Special-purpose computers (Majority)  Hardwired to do specific tasks only (usually one)  i.e. execute one program  Ubiquitous --- we interact with them almost daily --- embedded  Examples?  General-purpose computers  Provide means to change their programs thus becoming multi- or general-purpose machines  Include desktops, laptops/notebooks, servers, etc…  People tend to associate the word “computer” only with them  Limit ourselves to the latter type only

4. Software  Software is the set of all programs that run on a computer  VS Hardware : “Hard”  Has a physical presence  Comes in two forms  System Software: controls the computer  Applications Software: accomplishes user-defined tasks

5. Programs & Algorithms  Characteristics of an algorithm :  List of steps to complete a task  Each step is PRECISELY defined and is suitable for the machine used  Increase the value of X  Jump!  Add 5 to variable X  The process terminates in a finite amount of time  No infinite loops  Written in an English-like language (Pseudocode)

6. Programs & Algorithms  Program: A formal representation of a method for performing some task  Written in a programming language understood by a computer  Detailed and very well-organized (computers just do what they are told)  Follows an algorithm … method for fulfilling the task  Plan to do something VS the actual performance

8. Course Theme  Consumer Credit Risk Prediction: the process of estimating the risk of loss due to a customer's non re-payment (default) on a consumer credit product, such as a mortgage, unsecured personal loan, credit card, overdraft etc...  Problem : Given information for a new credit applicant, predict whether to approve or deny credit

9. Sample Data Customer Credit score (300-850) Total income ($) Duration for present employment (years) Age (years) # of dependents Prediction (Class Label) A80061,045.909453YES B71072,123.533232YES C42029,000.001201NO D39022,972.332500NO E55088,920.001390NO F825101,245.3430410YES G58943,298.0911602NO ….

10. The k-NN Prediction Algorithm  If something walks like a duck, quacks like a duck, looks like a duck, it must be a duck!  In other words, find the “k” closest customers to the new applicant and use majority voting to predict the class label for that customer