Algorithms and Data Structures 2 Overview

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Questions and Answers

Which of the following is NOT a topic covered in the first week of this course?

Introduction

Sorting algorithms (correct)

Analysis techniques

Recursive algorithms

What percentage of the final grade is allocated to the final exam?

20%

60%

40%

80% (correct)

Which of the following is NOT a prerequisite for this course?

Experience with programming in Java

Knowledge of basic calculus

Understanding of object-oriented programming concepts

Familiarity with pseudocode (correct)

What is the main purpose of complexity analysis in this course?

To study the performance of algorithms (A) Signup and view all the answers

Which of the following courses uses concepts learned in ADS 2?

All of the above (D) Signup and view all the answers

What is the primary focus of the first part of the course?

Data structures (C) Signup and view all the answers

What is the primary focus of Algorithmics II?

Algorithms (B) Signup and view all the answers

Which of the following is NOT a key concept introduced in ADS 2?

Object-Oriented Programming (A) Signup and view all the answers

What is the main text used for the ADS 2 course?

Data Structures and Algorithms in Java by Goodrich et al (A) Signup and view all the answers

According to the slides, where is the first lecture held?

Main Building 255 Humanity LT (A) Signup and view all the answers

When do the weekly labs for ADS 2 begin?

Week 2, beginning 15th January (A) Signup and view all the answers

Where are the labs held for ADS 2?

Both BO 706 and Kelvin Building Lab 320 (D) Signup and view all the answers

How many lectures are planned for this ADS 2 course?

11 lectures, each given twice a week (C) Signup and view all the answers

What defines an algorithm?

A step-by-step procedure for solving a problem (B) Signup and view all the answers

Which of the following is NOT an example of an algorithm?

Binary tree creation (B) Signup and view all the answers

How can an algorithm be specified?

By using natural language and pseudocode (A) Signup and view all the answers

What is a key characteristic of the INSERTION-SORT algorithm?

It is an efficient algorithm for sorting a small number of elements (A) Signup and view all the answers

Which data structure is often associated with algorithm creation?

Arrays (C) Signup and view all the answers

What does the running time of an algorithm typically do?

Increases with the input size (C) Signup and view all the answers

In terms of memory, what does 'in-place' mean for an algorithm?

It only requires a fixed amount of extra memory (C) Signup and view all the answers

What is a property of a stable sorting algorithm like INSERTION-SORT?

It maintains the relative order of equal elements (D) Signup and view all the answers

Which of the following best describes the correctness of an algorithm?

It halts with correct output for any input instance (A) Signup and view all the answers

What is one common practical application of algorithms in the internet?

Public-key cryptography (D) Signup and view all the answers

Which of the following statements about algorithms is false?

There is one perfect data structure for all algorithms (B) Signup and view all the answers

An example of an algorithm that can find the greatest common divisor is:

Euclid’s algorithm (D) Signup and view all the answers

Which of the following best describes a practical use of algorithms in biology?

DNA analysis (A) Signup and view all the answers

Study Notes

Course Introduction

Course title: Algorithms and Data Structures 2
Lecturer: Dr. Michele Sevegnani
Department: School of Computing Science, University of Glasgow
Contact email: [email protected]
Course year: ADS 2, 2024

Course Structure

11 lectures, two per week
Lecture 1: Tuesdays at 11:00 AM in Main Building 255 Humanity LT
Lecture 2: Thursdays at 11:00 AM in Main Building 220AB Hunter Halls
1-hour weekly lab sessions
Lab sessions start in week 2 (beginning January 15th)
Labs will be held in BO 706 or Kelvin Building Lab 320 (locations may be subject to change)
Attendance at lectures and labs will be monitored

Course Materials

Lecture slides available on Moodle at the start of each week.
Video lectures available on Moodle.
Lab sheets available via Moodle.
Textbook recommendations:
- Goodrich, M.T., Roberto Tamassia, and Michael H. Goldwasser. Data structures and algorithms in Java. John Wiley & Sons, 2014.
- Cormen, T.H., et al. Introduction to algorithms. MIT press, 2009.
- Sedgewick, R. Algorithms in Java, Parts 1-4 (Fundamental Algorithms, Data Structures, Sorting, Searching). Addison Wesley, 2002.

Course Content

The course content is divided into weeks with various topics.
A table of topics is provided, listing the topics covered each week. Details below: Week 1 : Introduction, Analysis techniques, Recursive algorithms Week 2: Sorting algorithms, Analysis of recursive algorithms Week 3: Analysis of recursive algorithms, Introduction Week 4: Linked lists Week 5: Abstract Data Types Week 6: Trees Week 7: Binary search trees, Balanced trees Week 9: Maps Week 10: Hash tables Week 11: Dynamic programming, Greedy algorithms

Assessment

Final exam (80%)
Past exams will be reviewed during revision classes.
Two coursework assignments (20%, 10% each)
Coursework will be built upon work developed during tutorial sessions.

Course Context

First course in Algorithms and Data structures specifically for students with Java programming experience.
Covers new concepts, including data structures, ADTs (abstract data types), algorithms, pseudocode, and complexity analysis.
- Prerequisites include basic knowledge of Java and calculus.
Explains the use of data structures and algorithms is fundamental to other key courses such as Object-Oriented Software Engineering.
Projects work will be undertaken in levels 3 or 4.
Basic algorithms and data structures will be used in other key courses such as operating systems scheduling, networking routing protocols, and information retrieval.

Foundations

Introduction to Algorithms
- Definition: A step-by-step procedure to solve a problem in a finite amount of time.
- Examples: Sorting, string matching, finding the greatest common divisor (Euclid's algorithm).
Introduction to Data Structures
- Definition: A method for organizing data to facilitate access and modification.
- Examples: Arrays, linked lists, stack, binary trees.

Algorithm Analysis

Correctness: An algorithm is correct if it produces the correct output for every input instance.
Algorithm Efficiency: The running time of an algorithm (which is related to the input size) is important.
Development of effective methods to manage time and space is vital.
Algorithm analysis will be studied in later lessons. This analysis allows for the ability to predict the resources necessary to execute the algorithm.

Summary

The course covers algorithms and data structures, with an emphasis practical application to real-world problems using Java. Key topics include algorithm analysis, data structures, and their implementation. Practical examples will be given throughout the content.

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Description

This quiz covers the course structure and materials for Algorithms and Data Structures 2 offered in 2024 at the University of Glasgow. It includes details about lectures, lab sessions, and available resources to help students succeed in the course.