Introduction to Computation and Programming Using Python, 3e
with Application to Computational Modeling and Understanding Data
by Guttag
ISBN: 9780262542364 | Copyright 2021
Instructor Requests
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| PREFACE (pg. xv) | |
| ACKNOWLEDGMENTS (pg. xix) | |
| 1 GETTING STARTED (pg. 1) | |
| 1.1 Terms Introduced in Chapter (pg. 9) | |
| 2 INTRODUCTION TO PYTHON (pg. 11) | |
| Installing Python and Python IDEs (pg. 13) | |
| 2.2 The Basic Elements of Python (pg. 15) | |
| 2.2.1 Objects, Expressions, and Numerical Types (pg. 16) | |
| 2.2.2 Variables and Assignment (pg. 19) | |
| 2.3 Branching Programs (pg. 21) | |
| 2.4 Strings and Input (pg. 27) | |
| 2.4.1 Input (pg. 31) | |
| 2.4.2 A Digression about Character Encoding (pg. 32) | |
| 2.5 While Loops (pg. 33) | |
| 2.6 For Loops and Range (pg. 37) | |
| 2.7 Style Matters (pg. 41) | |
| 2.8 Terms Introduced in Chapter (pg. 42) | |
| 3 SOME SIMPLE NUMERICAL PROGRAMS (pg. 45) | |
| Exhaustive Enumeration (pg. 45) | |
| 3.2 Approximate Solutions and Bisection Search (pg. 50) | |
| 3.3 A Few Words about Using Floats (pg. 56) | |
| 3.4 Newton–Raphson (pg. 60) | |
| 3.5 Terms Introduced in Chapter (pg. 62) | |
| 4 FUNCTIONS, SCOPING, AND ABSTRACTION (pg. 63) | |
| Functions and Scoping (pg. 66) | |
| 4.1.1 Function Definitions (pg. 66) | |
| 4.1.2 Keyword Arguments and Default Values (pg. 70) | |
| 4.1.3 Variable Number of Arguments (pg. 72) | |
| 4.1.4 Scoping (pg. 73) | |
| 4.2 Specifications (pg. 78) | |
| 4.3 Using Functions to Modularize Code (pg. 82) | |
| 4.4 Functions as Objects (pg. 84) | |
| 4.5 Methods, Oversimplified (pg. 86) | |
| 4.6 Terms Introduced in Chapter (pg. 87) | |
| 5 STRUCTURED TYPES AND MUTABILITY (pg. 89) | |
| Tuples (pg. 89) | |
| 5.1.1 Multiple Assignment (pg. 91) | |
| 5.2 Ranges and Iterables (pg. 92) | |
| 5.3 Lists and Mutability (pg. 93) | |
| 5.3.1 Cloning (pg. 100) | |
| 5.3.2 List Comprehension (pg. 103) | |
| 5.4 Higher-Order Operations on Lists (pg. 105) | |
| 5.5 Strings, Tuples, Ranges, and Lists (pg. 107) | |
| 5.6 Sets (pg. 110) | |
| 5.7 Dictionaries (pg. 112) | |
| 5.8 Dictionary Comprehension (pg. 118) | |
| 5.9 Terms Introduced in Chapter (pg. 121) | |
| 6 RECURSION AND GLOBAL VARIABLES (pg. 123) | |
| Fibonacci Numbers (pg. 125) | |
| 6.2 Palindromes (pg. 128) | |
| 6.3 Global Variables (pg. 132) | |
| 6.4 Terms Introduced in Chapter (pg. 134) | |
| 7 MODULES AND FILES (pg. 135) | |
| Modules (pg. 135) | |
| 7.2 Using Predefined Packages (pg. 138) | |
| 7.3 Files (pg. 142) | |
| 7.4 Terms Introduced in Chapter (pg. 145) | |
| 8 TESTING AND DEBUGGING (pg. 147) | |
| Testing (pg. 148) | |
| 8.1.1 Black-Box Testing (pg. 149) | |
| 8.1.2 Glass-Box Testing (pg. 152) | |
| 8.1.3 Conducting Tests (pg. 154) | |
| 8.2 Debugging (pg. 156) | |
| 8.2.1 Learning to Debug (pg. 159) | |
| 8.2.2 Designing the Experiment (pg. 160) | |
| 8.2.3 When the Going Gets Tough (pg. 164) | |
| 8.2.4 When You Have Found “The” Bug (pg. 165) | |
| 8.3 Terms Introduced in Chapter (pg. 166) | |
| 9 EXCEPTIONS AND ASSERTIONS (pg. 167) | |
| Handling Exceptions (pg. 168) | |
| 9.2 Exceptions as a Control Flow Mechanism (pg. 174) | |
| 9.3 Assertions (pg. 176) | |
| 9.4 Terms Introduced in Chapter (pg. 176) | |
| 10 CLASSES AND OBJECT-ORIENTED PROGRAMMING (pg. 177) | |
| Abstract Data Types and Classes (pg. 178) | |
| 10.1.1 Magic Methods and Hashable Types (pg. 184) | |
| 10.1.2 Designing Programs Using Abstract Data Types (pg. 186) | |
| 10.1.3 Using Classes to Keep Track of Students and Faculty (pg. 187) | |
| 10.2 Inheritance (pg. 190) | |
| 10.2.1 Multiple Levels of Inheritance (pg. 194) | |
| 10.2.2 The Substitution Principle (pg. 196) | |
| 10.3 Encapsulation and Information Hiding (pg. 197) | |
| 10.3.1 Generators (pg. 203) | |
| 10.4 An Extended Example (pg. 206) | |
| 10.5 Terms Introduced in Chapter (pg. 212) | |
| 11 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY (pg. 213) | |
| Thinking about Computational Complexity (pg. 214) | |
| 11.2 Asymptotic Notation (pg. 218) | |
| 11.3 Some Important Complexity Classes (pg. 221) | |
| 11.3.1 Constant Complexity (pg. 221) | |
| 11.3.2 Logarithmic Complexity (pg. 221) | |
| 11.3.3 Linear Complexity (pg. 223) | |
| 11.3.4 Log-Linear Complexity (pg. 224) | |
| 11.3.5 Polynomial Complexity (pg. 224) | |
| 11.3.6 Exponential Complexity (pg. 226) | |
| 11.3.7 Comparisons of Complexity Classes (pg. 228) | |
| 11.4 Terms Introduced in Chapter (pg. 231) | |
| 12 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES (pg. 233) | |
| Search Algorithms (pg. 234) | |
| 12.1.1 Linear Search and Using Indirection to Access Elements (pg. 235) | |
| 12.1.2 Binary Search and Exploiting Assumptions (pg. 237) | |
| 12.2 Sorting Algorithms (pg. 241) | |
| 12.2.1 Merge Sort (pg. 243) | |
| 12.2.2 Sorting in Python (pg. 247) | |
| 12.3 Hash Tables (pg. 250) | |
| 12.4 Terms Introduced in Chapter (pg. 255) | |
| 13 PLOTTING AND MORE ABOUT CLASSES (pg. 257) | |
| Plotting Using Matplotlib (pg. 257) | |
| 13.2 Plotting Mortgages, an Extended Example (pg. 263) | |
| 13.3 An Interactive Plot for an Infectious Disease (pg. 271) | |
| 13.4 Terms Introduced in Chapter (pg. 279) | |
| 14 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS (pg. 281) | |
| Knapsack Problems (pg. 282) | |
| 14.1.1 Greedy Algorithms (pg. 283) | |
| 14.1.2 An Optimal Solution to the 0/1 Knapsack Problem (pg. 287) | |
| 14.2 Graph Optimization Problems (pg. 291) | |
| 14.2.1 Some Classic Graph-Theoretic Problems (pg. 297) | |
| 14.2.2 Shortest Path: Depth-First Search and Breadth-First Search (pg. 297) | |
| 14.3 Terms Introduced in Chapter (pg. 304) | |
| 15 DYNAMIC PROGRAMMING (pg. 305) | |
| Fibonacci Sequences, Revisited (pg. 306) | |
| 15.2 Dynamic Programming and the 0/1 Knapsack Problem (pg. 309) | |
| 15.3 Dynamic Programming and Divide-and-Conquer (pg. 319) | |
| 15.4 Terms Introduced in Chapter (pg. 320) | |
| 16 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION (pg. 321) | |
| Random Walks (pg. 322) | |
| 16.2 The Drunkard’s Walk (pg. 323) | |
| 16.3 Biased Random Walks (pg. 331) | |
| 16.4 Treacherous Fields (pg. 338) | |
| 16.5 Terms Introduced in Chapter (pg. 340) | |
| 17 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS (pg. 341) | |
| Stochastic Programs (pg. 342) | |
| 17.2 Calculating Simple Probabilities (pg. 344) | |
| 17.3 Inferential Statistics (pg. 346) | |
| 17.4 Distributions (pg. 366) | |
| 17.4.1 Probability Distributions (pg. 369) | |
| 17.4.2 Normal Distributions (pg. 371) | |
| 17.4.3 Continuous and Discrete Uniform Distributions (pg. 378) | |
| 17.4.4 Binomial and Multinomial Distributions (pg. 379) | |
| 17.4.5 Exponential and Geometric Distributions (pg. 381) | |
| 17.4.6 Benford’s Distribution (pg. 385) | |
| 17.5 Hashing and Collisions (pg. 386) | |
| 17.6 How Often Does the Better Team Win? (pg. 389) | |
| 17.7 Terms Introduced in Chapter (pg. 391) | |
| 18 MONTE CARLO SIMULATION (pg. 393) | |
| Pascal’s Problem (pg. 394) | |
| 18.2 Pass or Don’t Pass? (pg. 396) | |
| 18.3 Using Table Lookup to Improve Performance (pg. 401) | |
| 18.4 Finding ( (pg. 403) | |
| 18.5 Some Closing Remarks about Simulation Models (pg. 409) | |
| 18.6 Terms Introduced in Chapter (pg. 411) | |
| 19 SAMPLING AND CONFIDENCE (pg. 413) | |
| Sampling the Boston Marathon (pg. 414) | |
| 19.2 The Central Limit Theorem (pg. 422) | |
| 19.3 Standard Error of the Mean (pg. 427) | |
| 19.4 Terms Introduced in Chapter (pg. 430) | |
| 20 UNDERSTANDING EXPERIMENTAL DATA (pg. 431) | |
| The Behavior of Springs (pg. 431) | |
| 20.1.1 Using Linear Regression to Find a Fit (pg. 435) | |
| 20.2 The Behavior of Projectiles (pg. 443) | |
| 20.2.1 Coefficient of Determination (pg. 445) | |
| 20.2.2 Using a Computational Model (pg. 447) | |
| 20.3 Fitting Exponentially Distributed Data (pg. 449) | |
| 20.4 When Theory Is Missing (pg. 454) | |
| 20.5 Terms Introduced in Chapter (pg. 455) | |
| 21 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING (pg. 457) | |
| Checking Significance (pg. 458) | |
| 21.2 Beware of P-values (pg. 466) | |
| 21.3 One-tail and One-sample Tests (pg. 469) | |
| 21.4 Significant or Not? (pg. 471) | |
| 21.5 Which N? (pg. 474) | |
| 21.6 Multiple Hypotheses (pg. 476) | |
| 21.7 Conditional Probability and Bayesian Statistics (pg. 479) | |
| 21.7.1 Conditional Probabilities (pg. 481) | |
| 21.7.2 Bayes’ Theorem (pg. 483) | |
| 21.8 Terms Introduced in Chapter (pg. 486) | |
| 22 LIES, DAMNED LIES, AND STATISTICS (pg. 487) | |
| Garbage In Garbage Out (GIGO) (pg. 488) | |
| 22.2 Tests Are Imperfect (pg. 489) | |
| 22.3 Pictures Can Be Deceiving (pg. 490) | |
| Cum Hoc Ergo Propter Hoc (pg. 494) | |
| 22.5 Statistical Measures Don’t Tell the Whole Story (pg. 497) | |
| 22.6 Sampling Bias (pg. 499) | |
| 22.7 Context Matters (pg. 500) | |
| 22.8 Comparing Apples to Oranges (pg. 501) | |
| 22.9 Picking Cherries (pg. 502) | |
| 22.10 Beware of Extrapolation (pg. 502) | |
| 22.11 The Texas Sharpshooter Fallacy (pg. 503) | |
| 22.12 Percentages Can Confuse (pg. 507) | |
| 22.13 The Regressive Fallacy (pg. 508) | |
| 22.14 Statistically Significant Differences Can Be Insignificant (pg. 509) | |
| 22.15 Just Beware (pg. 510) | |
| 22.16 Terms Introduced in Chapter (pg. 510) | |
| 23 EXPLORING DATA WITH PANDAS (pg. 511) | |
| DataFrames and CSV Files (pg. 511) | |
| 23.2 Creating Series and DataFrames (pg. 514) | |
| 23.3 Selecting Columns and Rows (pg. 518) | |
| 23.3.1 Selection Using loc and iloc (pg. 520) | |
| 23.3.2 Selection by Group (pg. 524) | |
| 23.3.3 Selection by Content (pg. 525) | |
| 23.4 Manipulating the Data in a DataFrame (pg. 527) | |
| 23.5 An Extended Example (pg. 530) | |
| 23.5.1 Temperature Data (pg. 530) | |
| 23.5.2 Fossil Fuel Consumption (pg. 541) | |
| 23.6 Terms Introduced in Chapter (pg. 544) | |
| 24 A QUICK LOOK AT MACHINE LEARNING (pg. 545) | |
| Feature Vectors (pg. 549) | |
| 24.2 Distance Metrics (pg. 552) | |
| 24.3 Terms Introduced in Chapter (pg. 559) | |
| 25 CLUSTERING (pg. 561) | |
| Class Cluster (pg. 563) | |
| 25.2 K-means Clustering (pg. 566) | |
| 25.3 A Contrived Example (pg. 569) | |
| 25.4 A Less Contrived Example (pg. 576) | |
| 25.5 Terms Introduced in Chapter (pg. 583) | |
| 26 CLASSIFICATION METHODS (pg. 585) | |
| Evaluating Classifiers (pg. 586) | |
| 26.2 Predicting the Gender of Runners (pg. 591) | |
| 26.3 K-nearest Neighbors (pg. 593) | |
| 26.4 Regression-based Classifiers (pg. 599) | |
| 26.5 Surviving the Titanic (pg. 612) | |
| 26.6 Wrapping Up (pg. 618) | |
| 26.7 Terms Introduced in Chapter (pg. 618) | |
| PYTHON 3.8 QUICK REFERENCE (pg. 619) | |
John V. Guttag
John V. Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT.
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