Algorithmic Thinking with Python KTU BTech S1 UCEST 105 2024 scheme - Dr Binu V P

The Trial and Error method is a straightforward and practical problem-solving strategy where you try different solutions and learn from mistakes until you find one that works. Let’s break it down: What is the Trial and Error Method? Definition : Trial and Error involves experimenting with various approaches or solutions until you achieve the desired result. If one attempt doesn’t work, you try another, learning from each failure or success along the way. Purpose : This method is used when you don’t have a clear solution in mind and need to explore different possibilities. Steps in the Trial and Error Method: Identify the Problem : Clearly understand what you’re trying to solve. Define the problem and your goal. Try a Possible Solution : Start with one approach or solution. This could be based on your intuition, past experience, or simply a guess. Test the Solution : Implement the solution and observe what happens. Does it solve the problem? Analyze the Results : If the solution works,

 Means-End Analysis is a problem-solving strategy where you break down the problem into a series of steps (means) to reach a desired outcome (end). Here’s how it works: What is Means-End Analysis? Means-End Analysis involves identifying the current state (where you are now) and the goal state (where you want to be). The key idea is to reduce the difference between these two states by applying specific actions or steps (means) that bring you closer to the goal. Steps in Means-End Analysis: Identify the Goal (End): Clearly define what you want to achieve. This is the end state you are aiming for. Assess the Current State: Understand your current position or condition relative to the goal. Identify the gap or difference between where you are and where you want to be. Identify the Differences: Determine the differences between the current state and the goal state. These differences highlight what needs to be changed or achieved to reach the goal. Select and Apply an Action (Means): Choose

The  Trial and Error  method involves trying different solutions until you find one that works. This method is especially useful when you don't have a clear path to the solution and need to experiment with various approaches. Here are some examples: 1.  Learning to Ride a Bicycle : Example : When learning to ride a bike, you might fall several times before figuring out how to balance and steer properly. Each attempt helps you learn what works and what doesn’t, leading to success. 2.  Solving a Puzzle : Example : If you’re putting together a jigsaw puzzle, you might try several pieces in a spot before finding the one that fits. You test each piece until you find the correct one. 3.  Cooking Without a Recipe : Example : If you're cooking a dish without a recipe, you might experiment with adding different amounts of spices or ingredients until you achieve the desired taste. You keep adjusting based on the results of each attempt. 4.  Finding the Right Tool : Example : When assembl

Means-End Analysis  is a problem-solving strategy that involves breaking down a problem into smaller, manageable parts (means) and addressing each part to reduce the difference between the current state and the goal state (end). Here are some examples of how this strategy can be applied: 1.  Planning a Vacation : Example : Goal : Plan a vacation to a tropical island. Means : Determine Budget : Calculate how much you can spend on the trip. Choose Destination : Research and select a suitable tropical island within your budget. Book Flights : Find and book flights to the chosen destination. Book Accommodation : Reserve a hotel or rental property on the island. Plan Activities : Decide on activities and excursions to do during the trip. 2.  Writing a Research Paper : Example : Goal : Complete a research paper on machine learning algorithms. Means : Research Topic : Gather information and review literature on machine learning algorithms. Create an Outline : Organize the main sections of the

Backtracking  is a method used to solve problems by exploring potential solutions and systematically undoing decisions when they lead to invalid or unsatisfactory outcomes. Here are some examples of backtracking in various scenarios: 1.  Solving a Maze : Example : Imagine you're trying to navigate a maze. You start at the entrance and choose a path. If you reach a dead end or find that your current path leads you in circles, you backtrack to the last junction and try a different direction until you find the exit. 2.  N-Queens Problem : Example : The N-Queens problem involves placing N queens on an N x N chessboard so that no two queens threaten each other. You place a queen in a row, and if you find that placing more queens leads to a conflict, you backtrack by removing the last placed queen and trying a new position for it. 3.  Sudoku Puzzle : Example : In a Sudoku puzzle, you fill in numbers in a grid according to specific rules. If placing a number in a cell violates the Sudoku

  Download the latest release of Python from  www.python.org   depending on your OS Windows,Linux or Mac Complete the installation.This will also install IDLE( Integrated Development Environment). Visit  https://docs.python.org/3/library/idle.html  to get complete details of IDLE. 1.Open IDLE by clicking the application icon 2.Open File menu and click New File and type your first script ( Eg: print("welcome to Python") 3.Save your file with .py extension ( Eg:test.py) 4.From the  Run menu  click the  Run Module(  or press F5-short cut).This will run the script 5.From the  File  menu choose  Exit  to quit from IDLE How to Run Python Code Interactively A widely used way to run Python code is through an interactive session. To start a Python interactive session, just open a command-line or terminal and then type in python, or python3 depending on your Python installation, and then hit Enter. Here’s an example of how to do this on Linux: Open Terminal ( Control-Alt-T) $ python3  

SEMESTER S1 ALGORITHMIC THINKING WITH PYTHON (Common to All Branches) B.Tech 2024 –S1/S2 Course Code: UCEST105  Teaching Hours/Week (L: T:P: R) 3:0:2:0  Credits 4  ESE Marks 60 CIE Marks 40  Exam Hours 2 Hrs. 30 Min Prerequisites (if any) None  Course Type :Theory Course Objectives: 1. To provide students with a thorough understanding of algorithmic thinking and its practical applications in solving real-world problems. 2. To explore various algorithmic paradigms, including brute force, divide-and-conquer, dynamic programming, and heuristics, in addressing and solving complex problems. Course Outcome and ÇO-PO mapping SYLLABUS Module-I Contact Hours- 7 PROBLEM-SOLVING STRATEGIES:- Problem-solving strategies defined, Importance of understanding multiple problem-solving strategies, Trial and Error, Heuristics, Means-Ends Analysis, and Backtracking (Working backward). THE PROBLEM-SOLVING PROCESS:- Computer as a model of computation, Understanding the problem, Formulating a model, Developi