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Author:

Guttag, John, author.

Title:

Introduction to computation and programming using Python : with application to understanding data / John V. Guttag.

Edition:

Second edition.

Publisher:

The MIT Press,

Copyright Date:

2016

Description:

xv, 447 pages ; 23 cm

Subject:

Python (Computer program language)--Textbooks.
Computer programming--Textbooks.

Notes:

Includes index.

Contents:

Machine generated contents note: 24.6. Wrapping Up. 2.1.1. Objects, Expressions, and Numerical Types -- 2.1.2. Variables and Assignment -- 2.1.3. Python IDE's -- 2.2. Branching Programs -- 2.3. Strings and Input -- 2.3.1. Input -- 2.3.2. Digression About Character Encoding -- 2.4. Iteration -- 3.1. Exhaustive Enumeration -- 3.2. For Loops -- 3.3. Approximate Solutions and Bisection Search -- 3.4. Few Words About Using Floats -- 3.5. Newton-Raphson -- 4.1. Functions and Scoping -- 4.1.1. Function Definitions -- 4.1.2. Keyword Arguments and Default Values -- 4.1.3. Scoping -- 4.2. Specifications -- 4.3. Recursion -- 4.3.1. Fibonacci Numbers -- 4.3.2. Palindromes -- 4.4. Global Variables -- 4.5. Modules -- 4.6. Files -- 5.1. Tuples -- 5.1.1. Sequences and Multiple Assignment -- 5.2. Ranges -- 5.3. Lists and Mutability -- 5.3.1. Cloning -- 5.3.2. List Comprehension -- 5.4. Functions as Objects -- 5.5. Strings, Tuples, Ranges, and Lists -- 5.6. Dictionaries -- 6.1. Testing -- 6.1.1. Black-Box Testing -- 6.1.2. Glass-box Testing -- 6.1.3. Conducting Tests -- 6.2. Debugging -- 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 -- 7.1. Handling Exceptions -- 7.2. Exceptions as a Control Flow Mechanism -- 7.3. Assertions -- 8.1. Abstract Data Types and Classes -- 8.1.1. Designing Programs Using Abstract Data Types -- 8.1.2. Using Classes to Keep Track of Students and Faculty -- 8.2. Inheritance -- 8.2.1. Multiple Levels of Inheritance -- 8.2.2. Substitution Principle -- 8.3. Encapsulation and Information Hiding -- 8.3.1. Generators -- 8.4. Mortgages, an Extended Example -- 9.1. Thinking About Computational Complexity -- 9.2. Asymptotic Notation -- 9.3. Some Important Complexity Classes -- 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 -- 10.1. Search Algorithms -- 10.1.1. Linear Search and Using Indirection to Access Elements -- 10.1.2. Binary Search and Exploiting Assumptions -- 10.2. Sorting Algorithms -- 10.2.1. Merge Sort -- 10.2.2. Exploiting Functions as Parameters -- 10.2.3. Sorting in Python -- 10.3. Hash Tables -- 11.1. Plotting Using PyLab -- 11.2. Plotting Mortgages, an Extended Example -- 12.1. Knapsack Problems -- 12.1.1. Greedy Algorithms -- 12.1.2. Optimal Solution to the 0/1 Knapsack Problem -- 12.2. Graph Optimization Problems -- 12.2.1. Some Classic Graph-Theoretic Problems -- 12.2.2. Shortest Path: Depth-First Search and Breadth-First Search -- 13.1. Fibonacci Sequences, Revisited -- 13.2. Dynamic Programming and the 0/1 Knapsack Problem -- 13.3. Dynamic Programming and Divide-and-Conquer -- 14.1. Random Walks -- 14.2. Drunkard's Walk -- 14.3. Biased Random Walks -- 14.4. Treacherous Fields -- 15.1. Stochastic Programs -- 15.2. Calculating Simple Probabilities -- 15.3. Inferential Statistics -- 15.4. 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 -- 15.5. Hashing and Collisions -- 15.6. How Often Does the Better Team Win? -- 16.1. Pascal's Problem -- 16.2. Pass or Don't Pass? -- 16.3. Using Table Lookup to Improve Performance -- 16.4. Finding pi -- 16.5. Some Closing Remarks About Simulation Models -- 17.1. Sampling the Boston Marathon -- 17.2. Central Limit Theorem -- 17.3. Standard Error of the Mean -- 18.1. Behavior of Springs -- 18.1.1. Using Linear Regression to Find a Fit -- 18.2. Behavior of Projectiles -- 18.2.1. Coefficient of Determination -- 18.2.2. Using a Computational Model -- 18.3. Fitting Exponentially Distributed Data -- 18.4. When Theory Is Missing -- 19.1. Checking Significance -- 19.2. Beware of P-values -- 19.3. One-tail and One-sample Tests -- 19.4. Significant or Not? -- 19.5. Which N? -- 19.6. Multiple Hypotheses -- 20.1. Conditional Probabilities -- 20.2. Bayes' Theorem -- 20.3. Bayesian Updating -- 21.1. Garbage In Garbage Out (GIGO) -- 21.2. Tests Are Imperfect -- 21.3. Pictures Can Be Deceiving -- 21.4. Cum Hoc Ergo Propter Hoc -- 21.5. Statistical Measures Don't Tell the Whole Story -- 21.6. Sampling Bias -- 21.7. Context Matters -- 21.8. Beware of Extrapolation -- 21.9. Texas Sharpshooter Fallacy -- 21.10. Percentages Can Confuse -- 21.11. Statistically Significant Differences Can Be Insignificant -- 21.12. Regressive Fallacy -- 21.13. Just Beware -- 22.1. Feature Vectors -- 22.2. Distance Metrics -- 23.1. Class Cluster -- 23.2. K-means Clustering -- 23.3. Contrived Example -- 23.4. Less Contrived Example -- 24.1. Evaluating Classifiers -- 24.2. Predicting the Gender of Runners -- 24.3. K-nearest Neighbors -- 24.4. Regression-based Classifiers -- 24.5. Surviving the Titanic -- 24.6. Wrapping Up.

ISBN:

0262529629 (pbk. : alk. paper)
9780262529624 (pbk. : alk. paper)

OCLC:

(OCoLC)949922840

LCCN:

2016019367

Locations:

OVUX522 -- University of Iowa Libraries (Iowa City)