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Electives

Computer Science

Every elective Zooky offers for this major.

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Year 3 · Semester 1 · choose 1 of 1

  • Ready to learn · Level 3 · 6 points

    Secure Software Development

    Writing software that resists attack: threat modelling, common vulnerabilities and secure coding.

    Prerequisites: no set prerequisites

    What you'll learn (10 topics)
    • Threat modelling: STRIDE, boundaries and defensive controls
    • Dynamic memory: malloc, free and leaks
    • Secure coding: validation, sizes and memory boundaries
    • Database access from code: connections, ORMs, parameters and races
    • Common vulnerabilities: injection, XSS and object permissions
    • Sessions, cookies and authentication boundaries
    • Application cryptography and key lifecycles
    • Coverage criteria
    • Security testing and verification evidence
    • Security principles and least-privilege workflows

Year 3 · Semester 2 · choose 1 of 5

  • Ready to learn · Level 3 · 6 points

    Computer Graphics and Games

    How a picture, a ray of light and a game world are computed: transforms, projection, shading, ray tracing, animation, game physics and VR budgets.

    Prerequisites: no set prerequisites

    What you'll learn (10 topics)
    • The rendering pipeline
    • Transformations and scene hierarchies
    • Viewing, projection and clipping
    • Rasterisation and visibility
    • Textures, sampling and colour
    • Lighting and shading
    • Ray tracing and global illumination
    • Animation: interpolation, curves and skeletons
    • Game engines: loop, collision and physics
    • Virtual reality basics

  • Ready to learn · Level 3 · 6 points

    Artificial Intelligence

    Rational agents and problem formulation, uninformed and informed search, local search and constraint satisfaction, games and alpha-beta pruning, logic and inference, automated planning, Bayesian networks and decisions, and how generative AI works, where it helps and where it fails.

    Prerequisites: Data Structures and Algorithms

    What you'll learn (21 topics)
    • Events and probability
    • Agents and problem formulation
    • Graph traversal and nonnegative shortest paths
    • Heaps and indexed priority queues
    • Uninformed search
    • Informed search and heuristics
    • Path planning: A-star, potential fields and RRT
    • Graphs: degrees, paths, Euler and Hamilton
    • Trees, spanning trees and colouring
    • Local search and constraint satisfaction
    • Recursion and divide and conquer
    • Adversarial search and games
    • Logic and proof techniques
    • Knowledge representation and logical inference
    • Automated planning
    • The Bayes filter and Gaussian noise
    • Discrete random variables: binomial and Poisson
    • Uncertainty, Bayesian networks and decisions
    • Vectors, projections, lines and planes
    • Neural networks and backpropagation
    • Generative AI: how it works, uses and limits

  • Being built now · Level 3 · 6 points

    Computer Vision and Pattern Recognition

    How a computer recognises what is in a picture: gradient histograms, local binary patterns, support vector machines, bag of visual words and Fisher's linear discriminant, all worked by hand on tiny images and tables.

    Prerequisites: no set prerequisites

    What you'll learn (10 topics)
    • Image filtering and edges
    • Gradient histograms and the HOG descriptor
    • Local binary patterns and histogram distances
    • Learning from data and evaluation
    • Support vector machines
    • Bayes, neighbour and tree classifiers
    • Features, PCA and clustering
    • Bag of visual words and image search
    • Fisher's linear discriminant
    • Convolutional networks

  • Ready to learn · Level 3 · 6 points

    IT Research Methods and Thesis Skills

    How to do research in IT: forming a question, reviewing the literature, designing an experiment, analysing numbers and interviews, treating participants ethically and planning a thesis, all on invented studies.

    Prerequisites: no set prerequisites

    What you'll learn (5 topics)
    • Research questions and the literature review
    • Experimental design and threats to validity
    • Quantitative analysis
    • Qualitative methods
    • Ethics, privacy and thesis planning

  • Ready to learn · Level 2 · 6 points

    Digital Media: Audio, Image and Video

    How sound, pictures and video become numbers: sampling and aliasing, image size and compression, colour spaces, frame rates and codecs, and the notes, mixes and textures a game has to budget.

    Prerequisites: no set prerequisites

    What you'll learn (5 topics)
    • Sampling and digital audio
    • Digital images and compression
    • Colour spaces
    • Video: frame rates and codecs
    • Synthesis, mixing and asset pipelines