Syllabus - DATA611

DATA611 - Applied Machine Learning

Description:
This course explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.

Outcomes:

  • Explain various machine learning techniques, including advantages and disadvantages of each
  • Apply machine learning techniques to real world problems
  • Evaluate best practices in machine learning
  • Support fact-based decision-making

Required Text(s):

Raschka, S. and Mirjalili, V. (2017). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow (2nd ed.). Packt Publishing.