1 CS1001 Lecture 19. 2 Overview Midterm Midterm OOP Wrap-up OOP Wrap-up Functions, Hilbert’s Hotel Functions, Hilbert’s Hotel.

Slides:



Advertisements
Similar presentations
Automated Theorem Proving Lecture 1. Program verification is undecidable! Given program P and specification S, does P satisfy S?
Advertisements

CS4432: Database Systems II
Reasoning About Code; Hoare Logic, continued
Based on Rosen, Discrete Mathematics & Its Applications, 5e (c) Michael P. Frank Modified by (c) Haluk Bingöl 1/18 Module #0 - Overview.
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
04/23/13 Countability Discrete Structures (CS 173) Derek Hoiem, University of Illinois 1.
Section 1.6: Sets Sets are the most basic of discrete structures and also the most general. Several of the discrete structures we will study are built.
Functions, Pigeonhole Principle Lecture 14: Nov 4 A B f( ) =
CS2420: Lecture 1 Vladimir Kulyukin Computer Science Department Utah State University.
1 CS1001 Lecture Overview Java Programming Java Programming Midterm Review Midterm Review.
CS261 Data Structures Winter 2011 Professor Timothy Budd.
CS1001 Lecture 11. Overview Software Engineering Software Engineering Design Methodologies Design Methodologies Software Ownership Software Ownership.
CS1001 Lecture 6. Overview Homework 1 Homework 1 Memory, Data Storage Memory, Data Storage Architecture Comparisons Architecture Comparisons Computer.
Computability Thank you for staying close to me!! Learning and thinking More algorithms... computability.
Vocabulary word (put this word on the back of the card.) THIS WILL BE THE DEFINITION – FILL IN THE BLANKS ON THE VOCABULARY CARDS PROVIDED.
Cardinality of Sets Section 2.5.
C OURSE : D ISCRETE STRUCTURE CODE : ICS 252 Lecturer: Shamiel Hashim 1 lecturer:Shamiel Hashim second semester Prepared by: amani Omer.
MATH 224 – Discrete Mathematics
Foundations of Computing I CSE 311 Fall CSE 311: Foundations of Computing I Fall 2014 Lecture 1: Propositional Logic.
Introduction to Proofs
Math 3121 Abstract Algebra I Section 0: Sets. The axiomatic approach to Mathematics The notion of definition - from the text: "It is impossible to define.
Math Review Data Structures & File Management Computer Science Dept Va Tech July 2000 ©2000 McQuain WD 1 Summation Formulas Let N > 0, let A, B, and C.
Korea Advanced Institute of Science and Technology, Dept. of EECS, Div. of CS, Information Systems Lab. 1/10 CS204 Course Overview Prof.
Relationships Between Structures “→” ≝ “Can be defined in terms of” Programs Groups Proofs Trees Complex numbers Operators Propositions Graphs Real.
CSE 311 Foundations of Computing I Lecture 8 Proofs and Set Theory Spring
Computer Science School of Computing Clemson University Discrete Math and Reasoning about Software Correctness Joseph E. Hollingsworth
CS1001 Lecture 9. Overview Security Security HTML HTML.
MATH 224 – Discrete Mathematics
CompSci 102 Discrete Math for Computer Science
COMPSCI 102 Introduction to Discrete Mathematics.
1 Georgia Tech, IIC, GVU, 2006 MAGIC Lab Rossignac Lecture 09: SEQUENCES Section 3.2 Jarek Rossignac CS1050: Understanding.
CS6133 Software Specification and Verification
CSE 311 Foundations of Computing I Lecture 9 Proofs and Set Theory Autumn 2012 CSE
Chapter 2 With Question/Answer Animations. Section 2.1.
Computability Universal Turing Machine. Countability. Halting Problem. Homework: Show that the integers have the same cardinality (size) as the natural.
Great Theoretical Ideas in Computer Science.
CSE 311 Foundations of Computing I Lecture 26 Cardinality, Countability & Computability Autumn 2011 CSE 3111.
CompSci 102 Discrete Math for Computer Science February 7, 2012 Prof. Rodger Slides modified from Rosen.
1 Propositional Logic: Fundamental Elements for Computer Scientists 0. Motivation for Computer Scientists 1. Propositions and Propositional Variables 2.
1 CS1001 Lecture Overview Projects Projects More on Cantor’s Proofs More on Cantor’s Proofs Predicate Logic Predicate Logic.
Logical Thinking CS 104 9/12/11. Agenda Today  College Board survey reminder  Note: Simple “how to” guide on Scratch posted on eLearning  Review HW.
CSE 311 Foundations of Computing I Lecture 25 Pattern Matching, Cardinality, Computability Spring
CSE 311: Foundations of Computing Fall 2013 Lecture 8: Proofs and Set theory.
CSE 311: Foundations of Computing Fall 2014 Lecture 27: Cardinality.
CSE 311 Foundations of Computing I Lecture 8 Proofs Autumn 2012 CSE
Chapter 2 1. Chapter Summary Sets (This Slide) The Language of Sets - Sec 2.1 – Lecture 8 Set Operations and Set Identities - Sec 2.2 – Lecture 9 Functions.
What is Discrete Math?. “Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous.” mathematicalstructuresdiscrete.
Section 1.7. Section Summary Mathematical Proofs Forms of Theorems Direct Proofs Indirect Proofs Proof of the Contrapositive Proof by Contradiction.
Discrete Structures MT217 Lecture 01. Course Objectives Express statements with the precision of formal logic Analyze arguments to test their validity.
Proof And Strategies Chapter 2. Lecturer: Amani Mahajoub Omer Department of Computer Science and Software Engineering Discrete Structures Definition Discrete.
Applied Discrete Mathematics Week 1: Logic and Sets
COMP 283 Discrete Structures
CSE 311 Foundations of Computing I
Chapter 4 (Part 1): Induction & Recursion
Section 11.1 Sequences Part I
MATH 224 – Discrete Mathematics
Discrete Mathematics for Computer Science
Cardinality of Sets Section 2.5.
Discrete Structures for Computer Science
Introduction to Discrete Mathematics
CSE 311 Foundations of Computing I
CSE 311: Foundations of Computing
CSE 311 Foundations of Computing I
First Order Logic Rosen Lecture 3: Sept 11, 12.
Discrete Math for CS CMPSC 360 LECTURE 43 Last time: Variance
First-order (predicate) Logic
Computer Science cpsc322, Lecture 20
Cardinality Definition: The cardinality of a set A is equal to the cardinality of a set B, denoted |A| = |B|, if and only if there is a one-to-one correspondence.
SETS, RELATIONS, FUNCTIONS
Presentation transcript:

1 CS1001 Lecture 19

2 Overview Midterm Midterm OOP Wrap-up OOP Wrap-up Functions, Hilbert’s Hotel Functions, Hilbert’s Hotel

3 Goals Learn foundations of modern functions/etc Learn foundations of modern functions/etc

4 Assignments Brookshear: Ch 5.5, Ch 6.3/6.4, Ch 7 (especially 7.7) (Read) Brookshear: Ch 5.5, Ch 6.3/6.4, Ch 7 (especially 7.7) (Read) Read linked documents on these slides (slides will be posted in courseworks) Read linked documents on these slides (slides will be posted in courseworks)

5 Midterm Expected Mean was 70. Actual Mean was ~67 Expected Mean was 70. Actual Mean was ~67 All grades are B- or higher (good work) All grades are B- or higher (good work) –A/A- is >= 81 –B to B+ is 55 to 81 –B- is < 55

6 Grade Distribution 4 Homeworks: 28% 4 Homeworks: 28% Tech project: 16% Tech project: 16% Final Paper: 16% Final Paper: 16% Midterm: 17% Midterm: 17% Final: 23% Final: 23%

7 Sets Functions are really just maps from a set of things to another set of things Functions are really just maps from a set of things to another set of things –For Example, f(x) = 2x establishes the discrete map (1 =>2, 2=>4, 3=>6 …) Since f(1) = 2, f(2) = 4, f(3) = 6 Most functions we work with are continuous and work over the real numbers Most functions we work with are continuous and work over the real numbers

8 Propositional Logic Information definition: a proposition is a statement of fact Information definition: a proposition is a statement of fact –“It is raining” (english) Connectives: operators on propositions Connectives: operators on propositions –And, or, not, implies, if and only if Raining

9 Theories A Theory in propositional logic is a set of constants, functions, relations and axioms. A Theory in propositional logic is a set of constants, functions, relations and axioms. Example: (theory of ordered integers) Example: (theory of ordered integers) –Constants: non-negative integers –Function: +, Relation: < –Axioms:

10 Why? Why do computer scientists care? Why do computer scientists care? Because theories are specifications of a collection of structures Because theories are specifications of a collection of structures To reason about code correctness To reason about code correctness To enable code transformations To enable code transformations –Must preserve invariants

11 Key Idea Sets and mappings define a function Sets and mappings define a function Functions (along with axioms and relations) form theories Functions (along with axioms and relations) form theories Theories are the foundation of logic Theories are the foundation of logic Our entire system of logic is built on the axioms of arithmetic (+, -, etc) Our entire system of logic is built on the axioms of arithmetic (+, -, etc)

12 Sets A finite set holds some number of things. A finite set holds some number of things. An infinite set holds a concept, not a number. It holds an infinite number of things. An infinite set holds a concept, not a number. It holds an infinite number of things. Are all infinite sets equal in size? No! (Cantor) Are all infinite sets equal in size? No! (Cantor)

13 Hilbert’s Hotel Is the set of Real Numbers equal to the size of the set of Integers? In other words are there more integers than real numbers? What about fractions? Are there more Rational (fractional) numbers than integers? Is the set of Real Numbers equal to the size of the set of Integers? In other words are there more integers than real numbers? What about fractions? Are there more Rational (fractional) numbers than integers?