COMP5318: Machine Learning and Data Mining

University of Sydney• 2025 S2
In Progress
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MachineLearning
DataMining
Python
DeepLearning

Introduces machine learning and data mining techniques for classification, regression, clustering, and pattern discovery, with hands-on implementation and evaluation using Python libraries.

Learning Outcomes

  • Algorithm principles & scope: Understand core concepts, strengths/weaknesses, and applicability of ML methods for **classification, regression, clustering, and reinforcement learning tasks.
  • Hands-on design & evaluation: Gain practical experience **designing, implementing, and evaluating** ML algorithms with appropriate metrics, validation, and baselines.
  • Software proficiency: Use modern ML libraries and workflows (e.g., scikit-learn pipelines, model selection, visual analytics) to build reproducible analyses.
  • Communication: Present and interpret data, methods, and results clearly in written and verbal formats for technical and non-technical audiences.

Takeaways

Coming soon.