Introduction to Machine Learning

Assignments

There will be 9 homework assignments during the semester in addition to the exams. The assignments will consist of both theoretical and programming problems. Homework assignments will be released via a Piazza announcement explaining where to find the handout, starter code, LaTeX template, etc.

The links to the Homework Handouts and Homework Exit Polls will be provided below.

• Homework 1: Background Material (written / programming)
  • Handout
  • Overleaf Link
• Homework 2: Decision Trees (written / programming)
  • Handout
  • Overleaf Link
• Homework 3: KNN, Perceptron, and Linear Regression (written)
  • Handout
  • Overleaf Link
• Homework 4: Logistic Regression (written / programming)
  • Handout
  • Overleaf Link
• Homework 5: Neural Networks (written / programming)
  • Handout
  • Overleaf Link
• Homework 6: Generative Models (written)
  • Handout
  • Overleaf Link
• Homework 7: Hidden Markov Models (written / programming)
  • Handout
  • Overleaf Link
• Homework 8: Reinforcement Learning (written / programming)
  • Handout
  • Overleaf Link
• Homework 9: Learning Paradigms (written)
  • Handout
  • Overleaf Link

Tentative release dates and due dates are listed on the Schedule page.

Exams

There will be three exams. The links to the Practice Problems and Exam Exit Polls will be provided below.

• Exam 1 (in-person): Lectures 1-7
  • Practice Problems
  • Practice Problems (Solutions)
  • Exit Poll: Exam 1
• Exam 2 (in-person): Lectures 8-17
  • Practice Problems
  • Practice Problems (Solutions)
  • Poll: Course Evaluation
• Exam 3 (in-person): Lectures 18-27
  • Practice Problems
  • Practice Problems (Solutions)