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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 algorithms 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 a binary heap using Python's built-in list.
  • Describe heap property.
  • Write functions to do binary heap data structure operations.
  • Implement algorithm to restore the heap property.
  • Describe and implement the algorithm to build a heap.
  • Implement heapsort using iteration.
  • Define Big-O notation and other asymptotic notations.
  • Derive complexity of a code using Python 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.