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Learning Objectives

Week 1: Sorting Algorithms​

Concept Map​

By the end of these lessons, students should be able to:

  • Apply Python's procedural programming and basic data structures
  • Define and call functions
  • describe bubble sort and insertion sort algorithm and ways to optimise it.
  • Implement Bubble sort and Insertion sort algorithms to sort a sequence of number
  • Use print statements to debug code

Week 2: Analysing Programs​

Concept Map​

By the end of these lessons, students should be able to:

  • Implement binary heap using Python's built-in list
  • Describe heap property
  • Describe the steps to build heap
  • Implement algorithm to restore heap property
  • Write functions to do binary heap data structure operation
  • Implement heapsort using iteration
  • Define Big-O notation and other asymptotic notations
  • Derive complexity of a code using its computation model
  • Create plots from array data for visualising computational time
  • Measure computation time for bubble sort, insertion sort and heapsort

Week 3: Divide and Conquer​

Concept Map​

By the end of these lessons, students should be able to:

  • Solve problems using recursion
  • Identify problems that has recursive solutions
  • Identify base case and recursive case in a recursive problem and its solution
  • Explain and implement the recursive solution of Tower of Hanoi
  • Explain and implement merge sort algorithm
  • Derive solution for recurrence of tower of hanoi and merge sort using recursion-tree method
  • Measure computation time of merge sort and compare it with the other sort algorithms
  • Use helper function for a recursive solution
  • Compare recursive solution with that using iteration and discuss its advantages and disadvantages

Week 4: Object-Oriented Paradigm​

Concept Map​

By the end of these lessons, students should be able to:

  • Use object-oriented programming to abstract data
  • Create class definitions with initialization and other methods
  • Create stored property and computed property
  • Draw UML class diagram of a class with attributes and methods
  • Explain has-a relationship
  • Use object-oriented programming to implement computation
  • Draw UML class diagram for has-a relationship
  • Implement abstract data type for Stack, Queue using Object Oriented paradigm
  • Implement Queue using double Stack and discuss implementation impact on computation time

Week 5: Inheritance and Intro to Graph​

Concept Map​

By the end of these lessons, students should be able to:

  • Apply Stack and Queue for some applications
  • Inherit a class to create a child class
  • Explain is-a relationship
  • Draw UML diagram for is-a relationship
  • Override operators to extend parent's methods
  • Use Dictionary and OOP to represent graph
  • Define graph, vertices, edges and weights
  • Differentiate directed and undirected graphs
  • Define paths
  • Create a Vertex class and a Graph class
  • Represent graphs using adjacency-list representation or adjacency-matrix representation
  • Extend class Vertex and Graph for graph traversal algorithm

Week 6: Graph Traversal​

Concept Map​

By the end of these lessons, students should be able to:

  • Explain and implement breadth first search
  • Explain and implement depth first search

Week 8: Design of State Machines​

Concept Map​

By the end of these lessons, students should be able to:

  • Define a state machine
  • Implement an Abstract Base Class for State Machine using abc module
  • Define output function and next state function
  • Draw state transition diagram and time-step table
  • Implement output function and next state function inside get_next_values overridden method.
  • Apply breadth first search to perform state-space search

Week 9: Visualizing and Processing Data​

Concept Map​

By the end of these lessons, students should be able to:

  • Give example of linear regression and classification
  • Create a Pandas DataFrame and selecting data from DataFrame
  • Convert Pandas DataFrame to NumPy Array
  • Selecting data from NumPy Array
  • Use mathematical, statistical and linear algebra functions on NumPy Array
  • Creating new Numpy Arrays
  • Using Pandas library to read CSV file
  • Split data randomly into training set and testing set
  • Normalize data using z-normalization and min-max normalization
  • Create scatter plot and statistical plots like box plot, histogram, and bar plot

Week 10: Modelling Continuous Data​

Concept Map​

By the end of these lessons, students should be able to:

  • Write cost function of linear regression
  • Implement Gradient Descent algorithm for optimisation
  • Train linear regression model using gradient descent
  • Transform data for polynomial model
  • Evaluate linear regression model using r^2 and mean-squared-error
  • Evaluate and choose learning rate
  • Plot cost function over iteration time
  • Plot linear regression

Week 11: Classifying Categorical Data​

Concept Map​

By the end of these lessons, students should be able to:

  • Write cost function of logistic regression
  • Use logistic regression to calculate probabilities of binary classification
  • Train logistic regression model
  • Split data into training, validation, and testing set
  • Classify multi-class problems using one-vs-all technique
  • Calculate confusion matrix, precision, and recall.