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Introduction to Computation and Programming Using Python_ With Application to Understanding Data (MIT Press) - John V. Guttag
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2022-02-24 01:24:54
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  • COPYRIGHT
  • CONTENTS
  • PREFACE
  • ACKNOWLEDGMENTS
  • 1 GETTING STARTED
  • 2 INTRODUCTION TO PYTHON
    • 2.1.1 Objects, Expressions, and Numerical Types
    • 2.1.2 Variables and Assignment
    • 2.1.3 Python IDE’s
      • 2.3.1 Input
    • 2.3.2 A Digression About Character Encoding
  • 3 SOME SIMPLE NUMERICAL PROGRAMS
  • 4 FUNCTIONS, SCOPING, AND ABSTRACTION
    • 4.1.1 Function Definitions
    • 4.1.2 Keyword Arguments and Default Values
    • 4.1.3 Scoping
      • 4.3.1 Fibonacci Numbers
    • 4.3.2 Palindromes
  • 5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS
    • 5.1.1 Sequences and Multiple Assignment
      • 5.3.1 Cloning
    • 5.3.2 List Comprehension
  • 6 TESTING AND DEBUGGING
    • 6.1.1 Black-Box Testing
    • 6.1.2 Glass-box Testing
    • 6.1.3 Conducting Tests
      • 6.2.1 Learning to Debug
      • 6.2.2 Designing the Experiment
      • 6.2.3 When the Going Gets Tough
      • 6.2.4 When You Have Found “The” Bug
  • 7 EXCEPTIONS AND ASSERTIONS
  • 8 CLASSES AND OBJECT-ORIENTED PROGRAMMING
    • 8.1.1 Designing Programs Using Abstract Data Types
    • 8.1.2 Using Classes to Keep Track of Students and Faculty
      • 8.2.1 Multiple Levels of Inheritance
    • 8.2.2 The Substitution Principle
    • 8.3.1 Generators
  • 9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
    • 9.3.1 Constant Complexity
    • 9.3.2 Logarithmic Complexity
    • 9.3.3 Linear Complexity
    • 9.3.4 Log-Linear Complexity
    • 9.3.5 Polynomial Complexity
    • 9.3.6 Exponential Complexity
    • 9.3.7 Comparisons of Complexity Classes
  • 10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES
    • 10.1.1 Linear Search and Using Indirection to Access Elements
    • 10.1.2 Binary Search and Exploiting Assumptions
      • 10.2.1 Merge Sort
      • 10.2.2 Exploiting Functions as Parameters
    • 10.2.3 Sorting in Python
  • 11 PLOTTING AND MORE ABOUT CLASSES
  • 12 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS
    • 12.1.1 Greedy Algorithms
    • 12.1.2 An Optimal Solution to the 0/1 Knapsack Problem
      • 12.2.1 Some Classic Graph-Theoretic Problems
      • 12.2.2 Shortest Path: Depth-First Search and Breadth-First Search
  • 13 DYNAMIC PROGRAMMING
  • 14 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION
  • 15 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS
    • 15.4.1 Probability Distributions
    • 15.4.2 Normal Distributions
    • 15.4.3 Continuous and Discrete Uniform Distributions
    • 15.4.4 Binomial and Multinomial Distributions
    • 15.4.5 Exponential and Geometric Distributions
    • 15.4.6 Benford’s Distribution
  • 16 MONTE CARLO SIMULATION
  • 17 SAMPLING AND CONFIDENCE INTERVALS
  • 18 UNDERSTANDING EXPERIMENTAL DATA
    • 18.1.1 Using Linear Regression to Find a Fit
      • 18.2.1 Coefficient of Determination
    • 18.2.2 Using a Computational Model
  • 19 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING
  • 20 CONDITIONAL PROBABILITY AND BAYESIAN STATISTICS
  • 21 LIES, DAMNED LIES, AND STATISTICS
  • 22 A QUICK LOOK AT MACHINE LEARNING
  • 23 CLUSTERING
  • 24 CLASSIFICATION METHODS
  • PYTHON 3.5 QUICK REFERENCE
  • INDEX
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