1. General Concepts (1/2) True or False For the true/False answers, give a one sentence explanation of each answer; answers without explanation will not be given any points. a) Suppose we use polynomial features for linear regression, then the hypothesis is linear in the original features [T/F] Answer: false, it is linear in the new […]
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Announcements Reminder: ps5 out, due Thursday 11/5 • pset 4 grades up on blackboard by Monday 11/9 Midterm grades out! Unweighted midterm grades (Median = 78) 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 Graduate students did better overall Graduate Students (Median 82) 35 40 45 50 55
Announcements Reminder: ps4 self-grading form out, due Friday 10/30 • pset 5 out today 10/29, due 11/5 (1 week) • Midterm grades will go up by Monday (don’t discuss it yet) Support Vector Machines CS542 Machine Learning slides based on lecture by R. Urtasun http://www.cs.toronto.edu/~urtasun/courses/CSC2515/CSC2515_Winter15.html Support Vector Machine (SVM) • A maximum margin method, can
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2020/12/1 Continuous Assignment 2 Continuous Assignment 2 Submit Assignment Due 10 Dec by 12:00 Points 25 Submitting a text entry box or a file upload Available 12 Nov at 10:00 – 8 Jan 2021 at 23:59 about 2 months Assignment 2 (Released on November 12th at 10:00 am) 25 points in total, worth 25% of
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24. Virtual Memory: TLB and Caches EECS 370 – Introduction to Computer Organization – Fall 2020 Satish Narayanasamy EECS Department University of Michigan in Ann Arbor, USA © Narayanasamy 2020 The material in this presentation cannot be copied in any form without written permission Final Exam Online exam through Gradescope Practice exam on Gradescope will
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Linear models: Recap Linear models: I Perceptron score(y, x; ✓) = ✓ · f (x, y) I Na ̈ıve Bayes: log P(y|x; ✓) = log P(x|y; ) + log P(y; u) = log B(x) + ✓ · f (x, y) I Logistic Regression log P(y|x; ✓) = ✓ · f (x, y) log X exp
Convolutional Networks for text classification Convolutional Networks I A convolutional network is designed to identify indicative local indicators in a large structure, and combine them to produce a fixed size vector representation of the structure with a pooling function, capturing the local aspects that are most informative of the prediction task at hand. I A
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Transformer: Neural machine translation without recurrence I It is possible to replace recurrence with self-attention within the encoder and decoder, as in the transformer architecture X( s ) M h(i)=⇥ReLU ⇥z(i)+b +b m21m12 ↵(i) (⇥ h(i1)) n=1 ⇣ ⌘ z(i) = m m!nn For each token m at level i, we compute self-attention over the
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Solutions to Selected Exercises Problem Set 1.2, page 9 1. The lines intersect at (x, y) = (3, 1). Then 3(column 1) + I(column 2) = (4, 4). 3. These “planes” intersect in a line in four-dimensional space. The fourth plane nor- mally intersects that line in a point. An inconsistent equation like u +
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Solutions to Selected Exercises Problem Set 1.2, page 9 1. The lines intersect at (x, y) = (3, 1). Then 3(column 1) + I(column 2) = (4, 4). 3. These “planes” intersect in a line in four-dimensional space. The fourth plane nor- mally intersects that line in a point. An inconsistent equation like u +
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