Course Code: ANL308
Enquire

Synopsis

ANL308 Machine Learning Applications for Predictive Analytics equips students with the skills to develop supervised machine learning models for solving real-world problems. The course covers key concepts such as model construction, evaluation, selection, and deployment, ensuring students understand how to build and assess effective models under various application scenarios. The course also introduces deep learning techniques, enabling students to apply advanced methods for complex predictive tasks. By the end of the course, students will be proficient in conducting a supervised machine learning solution for real-world cases using Python and cloud services.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JAN

Topics

  • Introduction to Predictive Modelling
  • Applications of Predictive Modelling
  • Data Preparation for Predictive Modelling
  • Model Construction and Evaluation
  • Parameter Tuning
  • Logistic Regression for Making Predictions
  • Decision Trees: CART and C5.0
  • Bagging and Boosting
  • Random Forest
  • Support Vector Machine
  • Artificial Neural Networks
  • Deep Neural Networks

Learning Outcome

  • Discuss various aspects of applying machine learning for predictive analytics
  • Appraise the application of predictive analytics
  • Compare different machine learning methods for predictive analytics
  • Construct predictive models/results, including the use of AI-assisted approaches, as part of solutions to address business problems
  • Evaluate the performance of predictive models
  • Interpret the results or outputs of predictive models for addressing business problems
  • Verify the validity, reliability and limitations of AI-assisted predictive models and outputs
Full-time Programmes Part-time Programmes / CET Courses