CS 124 Lecture 2 In order to discuss algorithms effectively, we need to start with a basic set of tools. Here, we explain these tools and provide a few examples. Rather than spend time honing our use of these tools, we will learn how to use them by applying them in our studies of actual […]
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CS124 Lecture 3 Spring 2011 Graphs and modeling Formulating a simple, precise specification of a computational problem is often a prerequisite to writing a computer program for solving the problem. Many computational problems are best stated in terms of graphs. A directed graph G(V,E) consists of a finite set of vertices V and a set
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Section 4: Divide and Conquer & Dynamic Programming Haneul Shin February 2021 Table of Contents ● Divide and Conquer ○ Introduction ○ Merge sort ○ Karatsuba’s algorithm ● Dynamic Programming ○ Introduction ○ Fibonacci ○ Longest path problem ○ Knapsack problem ● Practice Problems Divide and Conquer Divide and Conquer Approach 1. Divide the problem
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Statistical Machine Learning Christian Walder Machine Learning Research Group CSIRO Data61 and College of Engineering and Computer Science The Australian National University Canberra Semester One, 2020. (Many figures from C. M. Bishop, “Pattern Recognition and Machine Learning”) Statistical Machine Learning ⃝c 2020 Ong & Walder & Webers Data61 | CSIRO The Australian National University Outlines
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CS 124 Section 4 Solutions Haneul Shin February 2021 1. Given an array a with n integers, design a divide and conquer algorithm to find the sum of the maximum sum subarray. This is a subarray with the maximum possible sum, which may be empty. For instance, for a = [2, 3, -7, 1, 5,
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Section 5: Advanced Dynamic Programming CS 124 March 3, 2021 Outline 1 Dynamic Programming Review 2 Dynamic Programming on Subsets Example: Team Formation Bitmasks 3 Dynamic Programming on Trees Introduction Example: Maximum Weighted Matching 4 Problems Problem 1: Optimal Taxation Problem 2: Stack the Blocks Problem 3: Longest Paths in a Tree CS 124 Advanced
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CS 124 Programming Assignment 1: Spring 2021 Your name(s) (up to two): Collaborators: (You shouldn’t have any collaborators but the up-to-two of you, but tell us if you did.) No. of late days used on previous psets: No. of late days used after including this pset: Homework is due Wednesday 2021-02-24 at 11:59pm ET. You
Amortized Analysis and Hashing Ted Pyne Table of Contents ¡ñ Amortized Analysis ¡ñ Hashing Amortized Analysis ¡ñ Normal: Require our data structure to be fast on every operation ¡ñ Amortized: Require our data structure to be fast ¡°in total¡± Track runtime using potential function Cost of operation = real work + change in potential function
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Statistical Machine Learning Christian Walder Machine Learning Research Group CSIRO Data61 and College of Engineering and Computer Science The Australian National University Canberra Semester One, 2020. (Many figures from C. M. Bishop, “Pattern Recognition and Machine Learning”) Statistical Machine Learning ⃝c 2020 Ong & Walder & Webers Data61 | CSIRO The Australian National University Outlines
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CS 124 Programming Assignment 2: Spring 2021 Your name(s) (up to two): Collaborators: (You shouldn’t have any collaborators but the up-to-two of you, but tell us if you did.) No. of late days used on previous psets: No. of late days used after including this pset: Homework is due Wednesday 2021-03-24 at 11:59pm ET. You
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