Introduction to Machine Learning

Problem Sets

There will be 8 problem sets during the semester in addition to the exams. Problem sets will consist of both theoretical and programming problems.

• Homework 1: Background Exercises
  • Handout
• Homework 2: KNN, MLE, Naive Bayes
  • Handout
• Homework 3: Linear Regression and Logistic Regression
  • Handout
• Homework 4: Regularization, Kernel, Perceptron and SVM
  • Handout
• Homework 5: Researching Applications of Machine Learning
  • Handout
• Homework 6: Unsupervised Learning
  • Handout
• Homework 7: (Not yet released)
• Homework 8: (Not yet released)