Admission Open

Fundamentals of Algorithms Course in Mianwali

Course Title: Fundamentals of Algorithms
Module 1: Introduction to Algorithms

Understanding Algorithms

Definition and importance of algorithms
Characteristics of a good algorithm
Historical context and evolution of algorithms
Algorithm Design and Analysis

Steps in designing an algorithm
Analyzing algorithm efficiency
Big O notation, time complexity, and space complexity
Basic Data Structures

Arrays and linked lists
Stacks and queues
Trees and graphs
Lab Activities

Implementing basic algorithms (e.g., searching, sorting)
Analyzing the time and space complexity of simple algorithms
Exploring and manipulating basic data structures
Module 2: Sorting and Searching Algorithms

Sorting Algorithms

Bubble sort, insertion sort, selection sort
Merge sort, quick sort, heap sort
Comparison of sorting algorithms
Searching Algorithms

Linear search and binary search
Depth-first search (DFS) and breadth-first search (BFS)
Search efficiency in different data structures
Advanced Sorting and Searching

Radix sort, bucket sort
Interpolation search, exponential search
Practical applications and optimization
Lab Activities

Implementing and comparing sorting algorithms
Implementing and comparing searching algorithms
Analyzing and optimizing sorting and searching techniques
Module 3: Recursive Algorithms

Introduction to Recursion

Definition and principles of recursion
Base case and recursive case
Pros and cons of recursive algorithms
Classic Recursive Algorithms

Factorial and Fibonacci sequence
Tower of Hanoi
Recursive tree traversal (pre-order, in-order, post-order)
Recursion vs. Iteration

When to use recursion vs. iteration
Converting recursive algorithms to iterative
Tail recursion and optimization
Lab Activities

Implementing classic recursive algorithms
Converting recursive algorithms to iterative
Analyzing the performance of recursive algorithms
Module 4: Divide and Conquer

Principles of Divide and Conquer

Concept and methodology
Advantages and applications
Analyzing divide and conquer algorithms
Classic Divide and Conquer Algorithms

Merge sort and quick sort
Binary search
Closest pair of points
Advanced Applications

Strassen’s matrix multiplication
Fast Fourier Transform (FFT)
Karatsuba algorithm for fast multiplication
Lab Activities

Implementing divide and conquer algorithms
Analyzing and optimizing merge sort and quick sort
Exploring advanced divide and conquer applications
Module 5: Dynamic Programming

Introduction to Dynamic Programming

Principles and techniques
Memoization vs. tabulation
When to use dynamic programming
Classic Dynamic Programming Problems

Fibonacci sequence and factorial
Knapsack problem
Longest common subsequence (LCS)
Advanced Dynamic Programming

Matrix chain multiplication
Shortest path problems (Floyd-Warshall, Bellman-Ford)
Optimal binary search trees
Lab Activities

Implementing basic dynamic programming algorithms
Solving classic dynamic programming problems
Exploring advanced dynamic programming techniques
Module 6: Greedy Algorithms

Understanding Greedy Algorithms

Principles and methodology
Characteristics of greedy algorithms
When to use greedy algorithms
Classic Greedy Algorithms

Activity selection problem
Fractional knapsack problem
Huffman coding
Advanced Greedy Techniques

Prim’s and Kruskal’s algorithms for minimum spanning tree
Dijkstra’s algorithm for shortest path
Job sequencing problem
Lab Activities

Implementing basic greedy algorithms
Solving classic greedy problems
Analyzing the performance and correctness of greedy algorithms
Module 7: Graph Algorithms

Introduction to Graph Theory

Graph terminology and representation
Types of graphs (directed, undirected, weighted, unweighted)
Applications of graph algorithms
Classic Graph Algorithms

Depth-first search (DFS) and breadth-first search (BFS)
Topological sorting
Shortest path algorithms (Dijkstra, Bellman-Ford)
Advanced Graph Algorithms

Minimum spanning tree (Prim, Kruskal)
Network flow (Ford-Fulkerson)
Graph coloring and traversal
Lab Activities

Implementing graph traversal algorithms
Solving shortest path and minimum spanning tree problems
Exploring advanced graph algorithms and applications
Module 8: Final Project and Review

Project Planning and Proposal

Choosing a topic related to algorithm design and analysis
Defining project scope and objectives
Creating a project plan and timeline
Implementation and Development

Designing and implementing the chosen algorithm(s)
Testing and debugging
Optimizing and refining the algorithm
Final Presentation and Evaluation

Preparing a project presentation
Demonstrating the algorithm and its applications
Receiving and incorporating feedback
Review and Reflection

Reflecting on the learning experience
Identifying key takeaways and skills acquired
Planning for future learning and application of algorithms
Lab Activities

Developing a project proposal and plan
Implementing and testing the final project
Preparing and delivering the final presentation

Admission Open for this course 
Contact Number: 03307615544

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