This course provides an introduction to the fundamental methods and algorithms at the core of modern machine learning. It also covers theoretical foundations as well as essential algorithms and practical techniques for supervised and unsupervised learning.

Topics (tentative):

  • Introduction to Machine Learning and Supervised Learning
  • Regression
  • Parametric/Non Parametric Learning
  • Discriminative and Generative Algorithms
  • Naive Bayes, Non-linear Classifiers
  • Feature Engineering/Representation
  • Ensemble Methods
  • Support Vector Machine (SVM)
  • Unsupervised Learning and Clustering Algorithms
  • Principal Component Analysis, Independent Component Analysis
  • Neural Networks
  • Training, Testing and Evaluation
  • Reinforcement Learning
  • Lectures: Tuesday, Thursday 08:00 - 9:50 PM
  • Recitations: Friday 04:00 - 04:50 PM
  • Discussion: Piazza