Computer Science 230 Computer Architecture and Assembly Language Fall 2020 Assignment 3 Due: Monday, November 23th, 11:55 pm by conneX submission (Late submissions not accepted) Programming environment For this assignment you must ensure your work executes correctly on the MIPS Assembler and Runtime Simulator (MARS) as was installed during Assignment #0. Assignment submissions prepared with […]
Announcements Reminder: pset5 self-grading form and pset6 out today, due 11/19 (1 week) Unsupervised Learning III: Anomaly Detection Machine Learning Anomaly detection • What is anomaly detection? • Methods: – Density estimation – Detection by reconstruction – One-class SVM What is an anomaly? Anomaly Detection is • An unsupervised learning problem (data unlabeled) • About
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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: self-grading forms for ps1 and ps2 due 10/5 at midnight (Boston) • ps3 out on Thursday, due 10/8 (1 week) • LAB this week: go over solutions for the first two homeworks Agglomerative Clustering Example (bottom-up clustering) Image source: https://en.wikipedia.org/wiki/Hierarchical_clustering K-Means for Image Compression 3 Choose subspace with minimal “information loss” 𝑢(1) ∈
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Machine Learning Introduction Bryan Plummer Slides adapted from Kate Saenko Saenko 1 8 year-gap about me A.S., MCC B.S. & PhD, UIUC At BU 2018- Tenure Track 2020- • Research: Artificial Intelligence – Deep Learning for Vision – Vision and language understanding – Representation learning, Explainable AI, Efficient Neural Networks 2 Today • What is
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Announcements Reminder: self-grading forms for ps1 and ps2 due 10/5 at midnight (Boston) • ps3 out today, due 10/8 (1 week) • Midterm practice questions out next week Today: Outline • Neural networks cont’d: learning via gradient descent; chain rule review; gradient computation using the backpropropagation algorithm Machine Learning 2017, Kate Saenko 2 Neural Networks
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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: ps2 due Thursday at midnight (Boston) • Self-Grading form for ps1 out Friday 9/25 (1 week to turn in) • Self-Grading form for ps2 out Monday 9/28 (1 week to turn in) • Lab this week (no more rotations) – Linear/Logistic Regression, Anaconda Unsupervised Learning I 2 Today • Unsupervised learning – K-Means
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Supervised Learning III Classification, Regularization Detecting overfitting Plot model complexity versus objective function on test/train data As model becomes more complex, performance on training keeps improving while on test data it increases Horizontal axis: measure of model complexity In this example, we use the maximum order of the polynomial basis functions. Vertical axis: For regression,
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FIT9136 Algorithm and programming foundation in Python Assignment 2 Lecturer in Charge: Maghool April 2022 Copyright By PowCoder代写 加微信 powcoder Table of Contents 1. Key Information 1.1. Do and Do NOT 1.2. Documentation 1.3. Submission 2. Getting help 2.1. English language skills 2.2. Study skills 2.3. Things are tough right now 2.4. Things in the
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