Department of Electrical & Computer Engineering University of California, Davis EEC 170 – Computer Architecture Winter Quarter 2020 Laboratory Exercise 2: RISC-V Computer Arithmetic Due Date: Feb 19, 2019 Full Points 150 Objectives The objectives of this exercise are four fold. • You will learn how RISC-V assembly programming, especially the use of shift and […]
1 Overview 6CCS3ML1 (Machine Learning) Coursework 1 (Version 1.0) For this coursework, you will have to implement a classifier. You will use this classifier in some code that has to make a decision. The code will be controlling Pacman, in the classic game, and the decision will be about how Pacman chooses to move. Your
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CSCI-561 – Spring 2020 – Foundations of Artificial Intelligence Homework 2 Due March 24, 2020, 23:59:59 1. Overview In this programming homework, you will develop your own AI agents based on some of the learned AI techniques for Search, Game Playing, and Reinforcement Learning to play a small version of the Go game, called Go-5×5
CSCI 561 Foundation for Artificial Intelligence Advanced Game Playing Reinforcement Learning Professor Wei-Min Shen Outline • Motivation – Agent and Environment (Game) • States, actions, utility, rewards, policy • Utility value iteration • Policy Iterations • ReinforcementLearning – Model-based – Model-free • Q-Learning – State space for advanced game playing A Key Question • In
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Essentials of Compilation An Incremental Approach Jeremy G. Siek, Ryan R. Newton Indiana University with contributions from: Carl Factora Andre Kuhlenschmidt Michael M. Vitousek Michael Vollmer Ryan Scott Cameron Swords January 29, 2019 ii This book is dedicated to the programming language wonks at Indiana University. iv Contents 1 Preliminaries 5 1.1 AbstractSyntaxTreesandS-expressions. . .
 Please enter your full name here: This exam will test your knowledge in Data Science. We will test the following: • Basic knowledge in Data Science • How to manipulate data • How to clean data • Further data analysis 1. Basic Knowledge Test A) Create a dictionary and a list. In [31]: #YONGZHI WU,
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COMM7370 AI Theories and Applications¶ Search Algorithms¶ Uninformed Search¶ Implementation of the basic uninformed search algorithms using NetworkXlibrary In [1]: # Install NetworkX, Matplotlib, Pandas, Numpy using pip package in the current Jupyter kernel import sys !{sys.executable} -m pip install networkx !{sys.executable} -m pip install matplotlib !{sys.executable} -m pip install pandas !{sys.executable} -m pip install numpy
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Math 248: Computers and Numerical Algorithms Homework, Spring 2020 1. Change your working directory to your Flash drive, and enter the following Matlab program (call it adding.m): x = input(’Enter a value for x \n’); y = input(’Enter a value for y \n’); z=x+y; % computes the sum fprintf(’The sum of x and y is
4 Bias -Variance Tradeoff Probability studies uncertainty. Probability theory and statistics are often presented together, but they concern different aspects of uncertainty. Using probability we can consider a model of some process, where the underlying uncertainty is captured by random variables, and we use the rules of probability to derive what happens. In statistics we
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CSC242: Intro to AI Project 1: Game Playing This project is about designing an implementing an AI program that plays a game against human or computer opponents. You should be able to build a program that beats you, which is an interesting experience. The game for this term is Checkers, also known as English Draughts.