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Applied Machine Learning

Overview

  • Credit value: 15 credits at Level 7
  • Module convenor and tutor: Paul Yoo
  • Assessment: a 1000-word individual project report (40%) and two-hour examination (60%)

Module description

This module covers the fundamental concepts and techniques of applied machine learning using Python and how to use the existing tools to analyse data. You will develop the hands-on and practical skills needed for applied machine learning, including the use of existing Python libraries and tools (e.g. Scikit-Learn and TensorFlow) and the use of the techniques needed to analyse data (e.g. pre-processing, feature selection and classification). The module will use Python, the most popular machine learning language, to solve practical problems based on use cases extracted from real domains such as financial forecasting and computer vision.

indicative module Syllabus

  • Introduction to Python for machine learning
  • Preparing data
  • Feature selection for machine learning
  • Evaluation and resampling
  • Rule-based algorithms: decision tree and random forest
  • Regression-based algorithms: logistic regression and neural networks
  • Large-scale machine learning using TensorFlow
  • Real-life case studies (e.g. financial forecasting and computer vision)

Learning objectives

By the end of this module, you will be able to:

  • identify and use Python tools and libraries for machine learning based analytics tasks
  • evaluate and identify appropriate machine learning methods and techniques to analyse data
  • critically analyse and interpret machine learning results
  • use machine learning tools to solve practical problems in real-life scenarios
  • demonstrate deep understanding of a range of complex real-life topics in applied machine learning.