Information Technology Essentials - Lecture 02

Information Technology Essentials - Lecture 02

• Lecture title: Information Technology Essentials - Lecture 02
• Instructor: Dr. Karim Lounis
• Semester: Fall 2024
• Topics covered: Computer Systems, Concepts, notions, and terminologies in Computers
• Course location: ensia, National School of Artificial Intelligence

Computer Systems

• Definition: A system is a set of components that work together to achieve specific objectives or functions (e.g., provide a service).
• Examples of systems: immune system, power distribution system, supply chain management, computer system

What is a computer system?

• This topic is introduced, but not further defined in the provided text

Computers

• Definition: A computer is an electronic machine designed for automatically computing problems at a very high velocity
• Characteristics:
  • Electronic machines: Following the development of electronics
  • Automatic: Reducing human intervention
  • Compute: Solve problems
  • Problem: A question requiring a solution
• Further classifications:
  • Computable problems
  • Decidability
  • Uncomputable problems
  • Undecidable problems

How do computers try to solve problems?

• Steps to solving a problem using a computer:
  • Analyze and understand the problem
  • Propose a solution (algorithm)
  • Translate the algorithm into a program using a programming language
  • Feed the computer with the program and any required input
  • Convert the program to machine code so the computer can execute it
  • The computer swiftly executes the machine code and solves the problem
  • Display the results (the output)

Algorithm

• Definition: A finite sequence of steps to solve a problem.
• Characteristics of algorithms:
  • Finite set of steps
  • Unambiguous steps
  • Inputs and outputs
  • Termination
  • Feasibility
  • Expressed in natural language

Algorithm 1

• Problem statement: Given a list of random natural numbers, compute their product
• Steps:
  • Obtain the complete list of numbers
  • Multiply the first number by the second, and note the result
  • Multiply the result from step 2 by the next number on the list
  • Repeat step 3 until all numbers are exhausted
  • Announce the final result

Algorithm 2

• Problem statement: Deliver a parcel (like an egg tray) to a customer using a drone
• Steps:
  • Obtain the customer's location
  • Check if the location is within the drone's range; if not, cancel delivery
  • Load the drone with the parcel
  • Take off and fly to the customer's location
  • Avoid collisions during flight using sensors
  • Inform the customer that the drone is approaching
  • Gently release the parcel using a parachute
  • Confirm successful delivery with the customer
  • Return to storage, or continue with other deliveries
  • Notify the customer service of the delivery result

Algorithm 3

• Problem statement: Salim wants to send confidential documents to Ali in a locked box without revealing the key to Ali or the postman
• Steps:
  • Put the documents in a box and lock it
  • Send the box to Ali
  • Postman will collect the box and take it to Ali
  • Ali locks the box again with his own lock
  • Send the box to Salim
  • Postman will collect the box and take it to Salim
  • Salim removes his lock from the box
  • Postman will take the box and transport it back to Ali
  • Ali removes his lock and open the box to get the documents

Algorithms, Computability, & Uncomputability

• Computability: Whether a problem can be solved by an algorithm
• Decidability: Problems for which an algorithm can be constructed
• Uncomputable Problems: Problems for which algorithms cannot be constructed to solve in a finite amount of time

Algorithms & Complexity

• Complexity: Difficulty in solving problems by looking at solvability, requirements (space, time), efficiency, and classification
• Space complexity: Amount of memory needed to solve a problem
• Time complexity: Amount of time needed to solve a problem (worst-case scenario)
• Big-O notation: Method for classifying the time and space complexity of algorithms (e.g., O(1), O(log n), O(n), O(n log n), O(n^2))

Algorithms (Example)

• Problem statement: Display numbers from 1 to 100. For multiples of 3, print "EN". For multiples of 5, print “SIA". For multiples of both 3 and 5, print “ENSIA”
• The problem is solvable. An algorithm can be constructed.