ML2 - Courses - CeNCE

ML2 - Intermediate Machine Learning

For signups and further details, please contact Ms. Irene Ong Hwei Nee.

This MOOC-style course builds on the ML1 course by extending classical machine learning concepts into a statistical learning framework. It further introduces neural networks and advanced deep learning models, including CNNs and RNNs. The course concludes with a mini-project, enabling students to integrate and apply knowledge gained across both ML1 and ML2 in a practical, problem-solving context.

  • The course comprises of 7 lectures (each up to 60 mins long).
  • 7 auto-graded programming labs that complement the lectures.
  • 1 mini-project.
  • The Final Contest will be a Kaggle/Coursemology based competition along with a few MCQs.
  • Students are expected to follow the learning flow at their own pace to prepare themselves for the Final Contest.
  1. Statistics and Bayes Rule
  2. Statistical Machine Learning
  3. Neural Networks
  4. Convolutional Neural Networks (CNN) — Part 1
  5. Convolutional Neural Networks (CNN) — Part 2
  6. Recurrent Neural Networks (RNN) — Part 1
  7. Recurrent Neural Networks (RNN) — Part 2