Introduction to Computing
Explorations in Language, Logic, and Machines
David Evans

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Computer science studies how to describe, predict properties of, and efficiently implement information processes. This book introduces the most important ideas in computing using the Scheme and Python programming languages. It focuses on how to describe information processes by defining procedures, how to analyze the costs required to carry out a procedure, and the fundamental limits of what can and cannot be computed mechanically.

Errata (last update: 24 June 2019)


Front Matter [PDF]
Preface [PDF]


Chapter 1: Computing [PDF]
1.1 Processes, Procedures, and Computers
1.2 Measuring Computing Power (Information, Representing Data, Growth of Computing Power)
1.3 Science, Engineering, and the Liberal Arts
1.4 Summary and Roadmap

Exercises and solutions: PDF

Part I: Defining Procedures

Chapter 2: Language [PDF]
2.1 Surface Forms and Meanings
2.2 Language Construction
2.3 Recursive Transition Networks
2.4 Replacement Grammars
2.5 Summary

Exercises and solutions: PDF

Chapter 3: Programming [PDF]
3.1 Problems with Natural Languages
3.2 Programming Languages
3.3 Scheme
3.4 Expressions (Primitives, Application Expressions)
3.5 Definitions
3.6 Procedures (Making Procedures, Substitution Model of Evaluation)
3.7 Decisions
3.8 Evaluation Rules
3.9 Summary

Exercises and solutions: PDF

Chapter 4: Problems and Procedures [PDF]
4.1 Solving Problems
4.2 Composing Procedures (Procedures as Inputs and Outputs)
4.3 Recursive Problem Solving
4.4 Evaluating Recursive Applications
4.5 Developing Complex Programs (Printing. Tracing)
4.6 Summary

Exercises and solutions: PDF

Chapter 5: Data [PDF]
5.1 Types
5.2 Pairs (Making Pairs, Triples to Octuples)
5.3 Lists
5.4 List Procedures (Procedures that Examine Lists, Generic Accumulators, Procedures that Construct Lists)
5.5 Lists of Lists
5.6 Data Abstraction
5.7 Summary of Part I

Exercises and solutions: PDF

Part II: Analyzing Procedures

Chapter 6: Machines [PDF]
6.1 History of Computing Machines
6.2 Mechanizing Logic (Implementing Logic, Composing Operations, Arithmetic)
6.3 Modeling Computing (Turing Machines)
6.4 Summary

Chapter 7: Cost [PDF]
7.1 Empirical Measurements
7.2 Orders of Growth (Big O, Omega, Theta)
7.3 Analyzing Procedures (Input Size, Running Time, Worst Case Input)
7.4 Growth Rates (No Growth: Constant Time, Linear Growth, Quadratic Growth, Exponential Growth, Faster than Exponential Growth, Non-terminating Procedures)
7.5 Summary

Chapter 8: Sorting and Searching [PDF]
8.1 Sorting (Best-First Sort, Insertion Sort, Quicker Sorting, Binary Trees, Quicksort)
8.2 Searching (Unstructured Search, Binary Search, Indexed Search)
8.3 Summary

Part III: Improving Expressiveness

Chapter 9: Mutation [PDF]
9.1 Assignment
9.2 Impact of Mutation (Names, Places, Frames, and Environments; Evaluation Rules with State)
9.3 Mutable Pairs and Lists
9.4 Imperative Programming (List Mutators, Imperative Control Structures)
9.5 Summary

Chapter 10: Objects [PDF]
10.1 Packaging Procedures and State (Encapsulation, Messages, Object Terminology)
10.2 Inheritance (Implementing Subclasses, Overriding Methods)
10.3 Object-Oriented Programming
10.4 Summary

Chapter 11: Interpreters [PDF]
11.1 Python (Python Programs, Data Types, Applications and Invocations, Control Statements)
11.2 Parser
11.3 Evaluator (Primitives, If Expressions, Definitions and Names, Procedures, Application, Finishing the Interpreter)
11.4 Lazy Evaluation (Lazy Interpreter, Lazy Programming)
11.5 Summary

Part IV: The Limits of Computing

Chapter 12: Computability [PDF]
12.1 Mechanizing Reasoning (Gödel's Incompleteness Theorem)
12.2 The Halting Problem
12.3 Universality
12.4 Proving Non-Computability
12.5 Summary

Indexes [PDF]

University of Virginia Course

Previous versions: Fall 2011 Edition, Spring 2010 Edition, Fall 2009 Edition, Spring 2009 Edition