Course: Algorithms & Problem-Solving

Code: 21063-CS

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Introduction

Algorithmic thinking and problem-solving are fundamental skills in computer science, mathematics, and engineering. This course provides a comprehensive introduction to algorithms and systematic problem-solving approaches, enabling students to develop robust solutions to computational problems using proven methods.

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Description

This course covers the principles of algorithms, their design, analysis, and applications to practical problems. It explores classic algorithmic strategies such as recursion, divide-and-conquer, dynamic programming, and greedy methods. Through lectures, interactive exercises, and real-world examples, learners gain the ability to conceptualize and implement efficient algorithms.

Objectives

• To introduce the theoretical and practical aspects of algorithms and problem-solving.

• To develop skills for analyzing the complexity and efficiency of algorithms.

• To enable students to design, implement, and evaluate solutions to computational problems.

• To foster critical thinking and systematic approaches to solving novel problems.

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Outlines in Detail

1. Introduction to Algorithms

   • Definition and significance

   • Historical context

   • Types of algorithms

2. Algorithmic Problem-Solving Approaches

   • Problem analysis

   • Problem decomposition

   • Abstraction and modeling

3. Algorithm Design Paradigms

   • Brute-force algorithms

   • Greedy algorithms

   • Divide-and-conquer

   • Dynamic programming

   • Backtracking and branch-and-bound

4. Complexity Analysis

   • Time and space complexity

   • Big O, Big Ω, and Big Θ notations

   • Analyzing best, average, and worst-case scenarios

5. Data Structures in Algorithm Design

   • Arrays, lists, stacks, queues

   • Trees and graphs

   • Hash tables and heaps

6. Practical Problem-Solving

   • Algorithm implementation in pseudocode and programming languages

   • Debugging and testing

   • Case studies and real-world applications

7. Algorithmic Thinking in Research and Industry

   • Current trends

   • Ethical considerations

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What You Will Learn

• Fundamental concepts of algorithms and their role in computer science.

• Systematic approaches to problem analysis and solution design.

• Core algorithmic paradigms and when to apply them.

• Methods for evaluating and comparing algorithmic efficiency.

• Practical skills in writing, testing, and debugging algorithms.

• Application of algorithms to real-world and research problems.

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Target Audience

• Undergraduate and graduate students in computer science, engineering, mathematics, or related fields.

• Professionals seeking to enhance algorithmic thinking and problem-solving abilities.

• Anyone interested in the foundational principles of computational problem-solving.

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Materials

• Textbook: “Introduction to Algorithms” (Cormen et al., 2009)

• Supplementary readings from review articles and peer-reviewed journals

• Lecture slides and recorded videos

• Online coding platforms (e.g., LeetCode, HackerRank)

• Sample problem sets and solutions

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Instruction Methods

• Interactive lectures with multimedia presentations

• In-class and online problem-solving workshops

• Collaborative group assignments

• Case study analyses

• Formative and summative assessments

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Time Frame

• Total duration: 8 weeks

• Weekly commitment: 3 hours (2 hours lecture, 1 hour workshop/problem-solving session)

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Course Format

• Hybrid: Combination of in-person and online sessions

• Modules: 8 weekly modules, each focused on specific topics

• Assessment: Quizzes, assignments, midterm project, and final exam

• Support: Online discussion forums and office hours

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Learning Outcomes

Upon completion of this course, students will be able to:

1. Explain fundamental concepts and paradigms of algorithm design.

2. Analyze and compare algorithmic solutions for efficiency and correctness.

3. Design and implement algorithms to solve a range of computational problems.

4. Critically evaluate algorithmic solutions in real-world contexts.

5. Demonstrate enhanced problem-solving and critical-thinking abilities in computational settings.

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