Lecture Schedule

  • Jan 5: Introduction and Administrivia: Slides

  • Jan 7: A Geometric View of Linear Algebra: Notes

  • Jan 12: – contd. –

  • Jan 14: Classification: Nearest Neighbors: Notes

  • Jan 19: – contd. –

  • Jan 21: Decision Trees Notes

  • Jan 26: No lecture.

  • Jan 28: Decision Trees contd.

  • Feb 2: – contd. –

  • Feb 4: Midterm

  • Feb 9: Perceptron Notes

  • Feb 11: Midterm discussion. Perceptron contd.

  • Feb 16: Finish Perceptron Notes Begin Kernels Notes

  • Feb 19: Kernels contd.

  • Feb 23: – contd. –

  • Feb 25: Finish Kernels. Begin Bias-Variance Notes

  • Mar 1: Problem Session.

  • Mar 3: No lecture.

  • Mar 8: Boosting Notes

  • Mar 10: Finish up Boosting. Practical tips on how to do Machine Learning.