Singapore University of Social Sciences

Machine Learning

Machine Learning (ENG335)


ENG335 Machine Learning introduces machine learning, covering both supervised and unsupervised algorithms. Bias-variance trade-off is discussed for selecting the appropriate models. Neural networks and convolutional neural networks are introduced. Students have the opportunity to deploy the algorithms and fine-tune the parameters. Students are required to develop and deploy a machine learning algorithm to solve a real-world challenge.

Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY


  • Introduction to machine learning
  • Supervised Learning
  • Linear and Logistic Regressions
  • Naive Bayes Learning
  • Support Vector Machines
  • Decision Trees and Random Forests
  • Bias-Variance Tradeoff
  • Unsupervised Learning
  • K-means clustering
  • Neural networks and Convolutional neural networks
  • Tensorflow for machine learning
  • Machine learning in the cloud

Learning Outcome

  • Prepare data for machine learning algorithms.
  • Construct support vector machines for classification.
  • Set up decision trees, random forest for classification.
  • Rate the performance of clustering algorithm.
  • Design neural network based classifiers
  • Propose suitable machine learning algorithms
  • Estimate the performance metrics of learning algorithms
  • Assess the impact of hardware performance on the machine learning algorithms
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