Fall 2020 AV 201 Flight Dynamics Course Project Given a simplified aircraft dynamic model, complete the following tasks: (a) Build a nonlinear simulation model using MATLAB/Simulink; (b) Select appropriate values of altitude and speed, find the corresponding trimmed point; (c) Find the linearized model at the trimmed point; (d) Set the trimmed point as the […]
程序代写代做代考 C Fall 2020 AV 201 Flight Dynamics Course Project Read More »
Neural Networks CMPUT 366: Intelligent Systems GBC §6.0-6.4.1 1. Recap 2. Nonlinear models 3. Feedforward neural networks Lecture Outline • • • Partial derivatives are derivatives of “frozen” function: ∂ f(x,y) = d (f)y=y(x) • ∂x dx Gradient of a function is a vector of all its partial derivatives: ∂ ∂x ∂ ∂y Recap:
程序代写代做代考 algorithm deep learning C Neural Networks CMPUT 366: Intelligent Systems Read More »
CMPUT 366 F20: Supervised Learning VI James Wright & Vadim Bulitko November 24, 2020 CMPUT 366 F20: Supervised Learning VI 1 Lecture Outline Convolutional Networks GBC 9.0-9.4 CMPUT 366 F20: Supervised Learning VI 2 Recap: Neural Networks x1 h1 x2 h2 Each unit’s inputs are outputs from previous layer’s units Single unit h: Inputs x,
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CMPUT 366 F20: Supervised Learning V James Wright & Vadim Bulitko November 19, 2020 CMPUT 366 F20: Supervised Learning V 1 Lecture Outline Overfitting PM 7.4 CMPUT 366 F20: Supervised Learning V 2 Overfitting The learner makes predictions based on regularities that occur in the training data but not in the underlying population failure to
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CMPUT 366 F20: Probabilistic Inference James Wright & Vadim Bulitko October 29, 2020 CMPUT 366 F20: Probabilistic Inference 1 Lecture Outline Variable elimination algorithm PM 8.4 CMPUT 366 F20: Probabilistic Inference 2 Checking Correctness of Belief Networks A belief network claims that: all nodes are independent of their non-descendants given their parents To check correctness
程序代写代做代考 algorithm game C Bayesian graph CMPUT 366 F20: Probabilistic Inference Read More »
Q1–Q2 RP, Q3–Q4 AE F29AI 1. Uninformed, Informed, and Game Tree Search (a) Consider the following grid representing the states and transitions in a search problem. States are labelled with letters. An agent can move between states provided the two states are adjacent and not blocked by a wall (the black squares). Diagonal movement is
Statistical Parsing Using Treebanks ANLP: Week 6, Unit 1 Shay Cohen Based on slides from ANLP 2019 Last class Recursive Descent Parsing Shift-Reduce Parsing CYK: For j > i + 1: j−1 Chart[A,i,j]= Chart[B,i,k]∧Chart[C,k,j] k=i+1 A→B C Seed the chart, for i +1 = j: Chart[A, i, i + 1]
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This Unit Accelerated Natural Language Processing Week 1/Unit 3 Computational Approaches to Morphology Sharon Goldwater (based on slides by Philipp Koehn) Sharon Goldwater ANLP Week 1/Unit 3 • What is a Finite State Machine, and what is the relationship between FSMs and regular languages? • How are FSMs and FSTs used for morphological recognition, analysis
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A Zoo of Parsing Algorithms ANLP: Week 5, Unit 4 Shay Cohen Based on slides from ANLP 2019 1/70 Recap: Syntax Two reasons to care about syntactic structure (parse tree): As a guide to the semantic interpretation of the sentence As a way to prove whether a sentence is grammatical or not But
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ECS 122A Midterm 2 Solutions Question 1 [0 pt] What is the current time including seconds, add the digits together i.e. 3:14 21 secs pm sum = 3 + 1 + 4 + 2 + 1 = 11 Take the number above % 3 + 2, let that be y. What is your y? We
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