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Intro to CSCI-130

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

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Intro to CSCI-130

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

  2. Layered Architecture

  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

  7. 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

  8. Sample Data

  9. 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

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