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

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

The problem-solving process with a computer as a model of computation involves several systematic steps, leveraging the computer's ability to perform complex calculations, store vast amounts of data, and execute instructions rapidly. Here’s a brief overview: Problem Definition : The first step is to clearly define the problem. This includes understanding the requirements, constraints, and the desired output. The problem should be broken down into smaller, manageable components. Algorithm Design : Once the problem is understood, the next step is to design an algorithm—a step-by-step procedure or set of rules to solve the problem. The algorithm should be efficient, both in terms of time and space complexity, and should be validated for correctness. Implementation : After designing the algorithm, it is translated into a computer program using a programming language. The program is composed of instructions that the computer can execute to perform the desired task. This involves coding,

Heuristic methods are practical, quick, and often intuitive strategies that people use to solve problems. These methods don’t guarantee a perfect solution, but they usually provide a good enough answer efficiently. Here are some examples: 1.  Rule of Thumb : Example : "Measure twice, cut once" is a rule of thumb used in carpentry to avoid mistakes. Instead of precisely calculating every measurement, this heuristic encourages careful checking to prevent errors. 2.  Availability Heuristic : Example : If you’re deciding whether to buy travel insurance, you might rely on recent news about airplane crashes. Because these events are vivid and easily remembered, you might overestimate the risk of flying and decide to purchase the insurance. 3.  Working Backwards : Example : If you’re trying to solve a maze, you might start at the end and work your way backward to the start. This can often reveal the correct path more easily than starting from the beginning. 4.  Anchoring Heuristic :

Python has become a popular programming language and is widely used for several key reasons: 1. Simplicity and Readability Python's syntax is designed to be readable and straightforward. This simplicity allows students to focus on learning programming concepts rather than getting bogged down by complex syntax rules. 2. Versatility Python is a general-purpose language, meaning it can be used for a wide range of applications. From web development and data analysis to artificial intelligence and automation, Python provides a versatile platform for various projects and career paths. 3. Strong Community Support Python has a large, active community. This means that students can easily find resources, tutorials, and forums to get help when they encounter problems. The extensive documentation also supports learning and development. 4. Extensive Libraries and Frameworks Python boasts a rich ecosystem of libraries and frameworks that simplify many tasks. For instance, libraries like NumPy a

Python was created by Guido Van Rossum  in late 1980 when he was working at CWI (Centrum Wiskunde & Informatica) which is a National Research Institute for Mathematics and Computer Science in Netherlands.Python got its name from a BBC comedy series from seventies- “Monty Python's Flying Circus”. The development of Python began in December 1989, and the first official release, Python 0.9.0, was made available in February 1991. Guido van Rossum created Python as a successor to the ABC language, with the intention of making a language that was easy to read and simple to use, while also being powerful and flexible. Python has since evolved significantly and has become one of the most popular programming languages in the world. Some of the features which make Python so popular are as follows: It is a general purpose programming language which can be used for both scientific and non scientific programming.  It is a platform independent programming language.  It is a very simple high