Matt Gormley Lecture 11 October 5, 2016

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Presentation transcript:

Matt Gormley Lecture 11 October 5, 2016 School of Computer Science 10-601 Introduction to Machine Learning Midterm Review Matt Gormley Lecture 11 October 5, 2016 Readings:

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

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

Outline Exam Logistics Sample Questions Q&A

Exam Logistics

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)

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

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

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

Sample Questions

Sample Questions

Sample Questions

Sample Questions

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Sample Questions Dataset

Sample Questions Dataset

Sample Questions Dataset

Sample Questions Dataset

Sample Questions

Samples Questions

Samples Questions

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Sample Questions

Sample Questions

Sample Questions

Sample Questions

Q&A

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

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