For Machine Learning Class

Introduction to Machine Learning

Logistics

Basic math

Linear algebra Probability

Supervised Learning

Linear / logistic regression Tree-based algorithms SVM

Unsupervised Learning

Clustering algorithms

Optimization

Convex/non-convex problems Optimization algorithms (first-order, second-order)

Neural Networks and Deep Learning

• perceptron
• multi-layer perceptron
• various types of neural networks
  
  

Model Selection and Performance Assessment

• cross validation
• accuracy / loss
  
  

Machine Learning use-cases

• Inverse problems
• Large-scale problems