Applied Machine Learning

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Course Description

Predictive data analytics and machine learning technologies are becoming increasingly important in industry, not only to IT companies, but to companies in all areas. This course gives students the necessary background and experience in applied machine learning technology and concepts. Students are expected to gain experience with tackling a complete predictive modelling project in Python, from data gathering and preprocessing to data analysis through machine learning tools. They will also learn fundamental concepts in machine learning, big data storage and skills in python, data wrangling as a foundation for their integrated project.

Learning Objectives

  • Be aware of the main goals of machine learning, its main application domains and current challenges
  • Apply tools to build basic models for solving typical predictive modelling problems
  • Visualise the structure of big data in order to uncover hidden patterns
  • Perform basic operations using distributed processing techniques
  • Explain the fundamentals of statistical machine learning and deep learning
  • Appreciate the technical skills necessary to be a capable machine learning scientist

Measurable Outcomes

  • Identify important concepts and current challenges in machine learning
  • Design feature representations for image, text and time series data
  • Analyse data and build machine learning models in Python
  • Evaluate the performance of different models using empirical benchmarks
  • Mathematically explain common machine learning models such as logistic regression systems and neural networks
  • Implement machine learning algorithms using Python
  • Manage big data using Hadoop and MapReduce



Mutually Exclusive Subject

Computational Data Science (50.038)