ExploreSign inStart learning
CodingYear 3Quality review 0/4

Computer Science Year 3

Theory of computation, introductory machine learning, computer networks and distributed systems.

≈ 17 h 4 min5 sections · 32 lessonsNo ratings yet
Start course

What you'll learn

  • Reason about computability
  • Evaluate a basic ML pipeline
  • Analyse layered network protocols
  • Explain distributed trade-offs

Before you start

  • Computer Science Year 2
  • Probability and discrete mathematics

Academic references

Use the latest available edition and your institution's prescribed text.

  • Python Software Foundation, The Python Language Reference
  • Cormen et al., Introduction to Algorithms
  • Downey, Think Python

Formal work standard

  • Trace state and invariants before running the program.
  • State preconditions, postconditions, and complexity where relevant.
  • Test normal, boundary, invalid, and adversarial inputs.

Course content

0/32 lessons
1. Foundations and Models4 lessons
  1. Theory of ComputationLesson32 min
  2. Machine Learning IntroductionLesson32 min
  3. Computer NetworksLesson32 min
  4. Distributed SystemsLesson32 min
2. Extended Study 18 lessons
  1. Theory of Computation: Concept MapLesson32 min
  2. Machine Learning Introduction: Concept MapLesson32 min
  3. Computer Networks: Concept MapLesson32 min
  4. Distributed Systems: Concept MapLesson32 min
  5. Theory of Computation: MechanismLesson32 min
  6. Machine Learning Introduction: MechanismLesson32 min
  7. Computer Networks: MechanismLesson32 min
  8. Distributed Systems: MechanismLesson32 min
3. Extended Study 28 lessons
  1. Theory of Computation: RepresentationsLesson32 min
  2. Machine Learning Introduction: RepresentationsLesson32 min
  3. Computer Networks: RepresentationsLesson32 min
  4. Distributed Systems: RepresentationsLesson32 min
  5. Theory of Computation: Worked ReasoningLesson32 min
  6. Machine Learning Introduction: Worked ReasoningLesson32 min
  7. Computer Networks: Worked ReasoningLesson32 min
  8. Distributed Systems: Worked ReasoningLesson32 min
4. Extended Study 38 lessons
  1. Theory of Computation: Evidence and MeasurementLesson32 min
  2. Machine Learning Introduction: Evidence and MeasurementLesson32 min
  3. Computer Networks: Evidence and MeasurementLesson32 min
  4. Distributed Systems: Evidence and MeasurementLesson32 min
  5. Theory of Computation: Applied ScenarioLesson32 min
  6. Machine Learning Introduction: Applied ScenarioLesson32 min
  7. Computer Networks: Applied ScenarioLesson32 min
  8. Distributed Systems: Applied ScenarioLesson32 min
5. Extended Study 44 lessons
  1. Theory of Computation: MisconceptionsLesson32 min
  2. Machine Learning Introduction: MisconceptionsLesson32 min
  3. Computer Networks: MisconceptionsLesson32 min
  4. Distributed Systems: MisconceptionsLesson32 min