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Introduction to Algorithms and Data Structures II Course
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What should be known before taking this course
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The following are covered in the prerequisite courses at UWF. If any of these
are not familiar, please review them before taking this course or when needed.
I will revisit some of them in this course as needed.

+ Development environment setup

  * Local

    - Linux native
    - Mac OS native
    - Windows system for Linux (WSL)

  * Remote

    - SSH server (Linux)

+ Basic programming skills in C++

  * Variables and types
  * Control flow
  * Functions
  * Pointers and references
  * Classes and objects
  * Templates
  * Standard library

+ Basic knowledge of of computational complexity

  * Big-O notation
  * Big-Omega notation
  * Big-Theta notation

+ Basic knowledge of data structures and abstract data types

  * Array
  * Linked list
  * Stack ADT
  * Queue ADT
  * Heap
  * Priority queue ADT
  * Tree

    - Binary tree
    - Binary search tree

  * Hash table
  * Map ADT
  * Set ADT
  * Graph

+ Knowledge of algorithms

  * Sorting

    - Insertion sort
    - Selection sort
    - Merge sort
    - Quick sort
    - Heap sort
    - Radix sort

  * Searching

    - Linear search
    - Binary search
    - Search in a search tree
    - Search in a hash table

  * Graph

    - Depth-first search
    - Breadth-first search
    - Single source shortest path: Dijkstra's algorithm

  * Brute force
  * Backtracking

+ Basic knowledge of mathematics

  * Computational complexity

+ Version control system (git) and GitHub classroom

What will be covered in this course
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+ Mathematical foundations

  * Basics

    - Exponent
    - Logarithm
    - Modular arithmetic
    - Proof methods

  * Recurrence relations and their solutions
  * Combinatorics and counting

    - Arrangement
    - Selection
    - Distribution
    - Addition principle
    - Multiplication principle
    - Inclusion-exclusion principle
    - Division principle
    - Pigeonhole principle

  * Probabilistic analysis

    - Random variables
    - Expectation
    - Events
    - Classic probability

+ Type of problems

  * Enumeration problem (find all solutions)
  * Search problem (find a solution)
  * Optimization problem (find the best solution)

+ Algorithmic classification

  * Brute force paradigm
  * Backtracking paradigm
  * Greedy paradigm
  * Divide-and-conquer paradigm
  * Dynamic programming paradigm
  * Evolutionary paradigm

    - Genetic algorithm

  * Exact and approximate algorithms
  * Randomized algorithms

    - Monte Carlo algorithm

  * Heuristic/metaheuristic algorithms

+ Core data structures and related algorithms

  * Self-balancing trees

    - AVL tree
    - B-tree

  * Graph

    - Minimal spanning tree
    - Shortest path

+ Classic problems and solutions

  * Traveling salesman problem
  * Bin packing problem
  * Huffman coding
  * Max sum subarray problem
  * Chain matrix multiplication
  * Longest common subsequence
  * Minimal spanning tree
  * Shortest path