1. Matt Gormley Lecture 11 October 5, 2016
School of Computer Science
Introduction to Machine Learning
Midterm Review
Matt Gormley
Lecture 11
October 5, 2016
Readings:

2. Reminders Midterm Exam Midterm Review Session Homework 4
Mon, Oct. 10th
Midterm Review Session
Thu, Oct. 6th at 6:00pm
Homework 4
Extension: due Fri (10/7) at 5:30pm
Homework 4 Review Session
Today (Wed), Oct. 5th at 6:00pm in NSH 3002
Please look for a (pending) announcement

3. Reminders Midterm Exam Midterm Review Session Homework 4
Mon, Oct. 10th
Midterm Review Session
Thu, Oct. 6th at 6:00pm
Homework 4
Extension: due Fri (10/7) at 5:30pm
Homework 4 Review Session
Today (Wed), Oct. 5th at 6:00pm in NSH 3002
Please look for a (pending) announcement

4. Outline
Exam Logistics
Sample Questions
Q&A

5. Exam Logistics

6. Midterm Exam In-class exam on Mon, Oct. 10th 5 problems
Format of questions:
Multiple choice
True / False (with justification)
Derivations
Short answers
Interpreting figures
No electronic devices
You are allowed to bring one 8½ x 11 sheet of notes (front and back)

7. Midterm Exam How to Prepare
Attend the midterm recitation session: Thu, Oct. 6th at 6:00pm
Review prior year’s exams and solutions (we’ll post them)
Review this year’s homework problems

8. Midterm Exam Advice (for during the exam)
Solve the easy problems first (e.g. multiple choice before derivations)
if a problem seems extremely complicated you’re likely missing something
Don’t leave any answer blank!
If you make an assumption, write it down
If you look at a question and don’t know the answer:
we probably haven’t told you the answer
but we’ve told you enough to work it out
imagine arguing for some answer and see if you like it

9. Topics for Midterm Foundations Classifiers Regression
Probability
MLE, MAP
Optimization
Classifiers
Decision Trees
Naïve Bayes
Logistic Regression
Perceptron
SVM
Regression
Linear Regression
Important Concepts
Kernels
Regularization and Overfitting
Sample Complexity
Experimental Design

10. Sample Questions

11. Sample Questions

12. Sample Questions

13. Sample Questions

14. Sample Questions

15. Sample Questions
Dataset

16. Sample Questions
Dataset

17. Sample Questions
Dataset

18. Sample Questions
Dataset

19. Sample Questions

20. Samples Questions

21. Samples Questions

22. Sample Questions

23. Sample Questions

24. Sample Questions

25. Sample Questions

26. Sample Questions

27. Q&A

28. Matching Game Goal: Match the Algorithm to its Update Rule
1. SGD for Logistic Regression
2. Least Mean Squares
3. Perceptron (next lecture) 4. 5. 6.
A. 1=5, 2=4, 3=6
B. 1=5, 2=6, 3=4
C. 1=6, 2=4, 3=4
D. 1=5, 2=6, 3=6
E. 1=6, 2=6, 3=6

29. Announcement