Machine Learning


Machine Learning is one of the most exciting and rapidly developing areas of technology. However it can be difficult to understand all the different technologies and techniques because they’re so diverse and require a broad range of prior knowledge.

This course is intended to provide a solid grounding that will enable the learner to feel confident delving into cutting edge technologies in Machine Learning.

The Course

Core Curriculum


  • Linear Algebra

  • Probability and Statistics

  • Optimization

Technological Foundations:

  • Data cleaning for Machine Learning

  • Architectures and Loss Functions

  • Probably Approximately Correct Learning

Applications and Case Studies:

  • Recommendation Systems

  • Self-driving Cars

  • Financial Models

  • Natural Language Processing

  • Image Processing

Introductory Artificial Intelligence:

  • Regression

  • Decision Trees

  • Dimensionality Reduction

  • Boosting Algorithms

  • Introductory Deep Learning


Specializations should be discussed with your Director of Studies. The following provide a brief list of directions one could take:

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning 

  • Bayesian Learning

  • Convolutional Neural Networks