Computer Science II: Programming Abstractions

Note: This course is being offered by Stanford this summer as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development.

This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java. If you've taken the Computer Science AP exam and done well (scored 4 or 5) or earned a good grade in a college course, Programming Abstractions may be an appropriate course for you to start with, but often Programming Abstractions (Accelerated) is a better choice. Programming Abstractions assumes that you already have familiarity with good programming style and software engineering issues (at the level of Programming Methodology), and that you can use this understanding as a foundation on which to tackle new topics in programming and data abstraction.

Topics: Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.

Prerequisites: Solid performance in Programming Methodology and readiness to move on to advanced programming topics. A comparable introductory programming course (including high school AP courses) is often a reasonable substitute for our Programming Methodology.

Lectures:

Lecture 1 (43:03)
About the Introduction to Computer Science Series at Stanford, The Philosophy, Why take CS106B?, Logistics of the Course, Introducing C++
Lecture 2 (43:48)
Similarity between C++ & Java: - syntax - variable types - operators - control structures, Looking at an Example C++ code: - comment, #include Statements, Global Declarations (constant), Declaring a Function Prototype, The main() Function, Decomposed Function Definition, Example Live Coding: To Calculate the Average, for loop -> a while : Another Purpose of the Same Code, C++ User Defined Data Types: -enums -records, C++ Parameters Passing: -pass by value - pass by reference
Lecture 3 (44:40)
C++ Libraries - Standard Libraries, CS106 Libraries, CS106 random.h Library, C++ String Type, Operations on String Type, String Class' Member Functions, C++ string vs Java String, Live Example Code : Working on Strings, CS106 strutils.h Library, C++ String vs C String, Concatenation Pitfall (C++ vs C string cont.), C++ Console I/O
Lecture 4 (50:27)
C++ Console I/O, C++ File I/O, Stream Operations, Live Example Coding : Working with Files, Live Coding Continuation: Function to Operate on the Opened File Stream, Passing the File Stream by Reference, Error Function, Class Libraries OO Features, Why OO is So Successful, CS106 Class Library, CS106: Scanner Library, Scanner Client Interface, Client Use of Scanner, Container Classes, Template Containers, Vector Interface
Lecture 5 (45:30)
Client Use of Templates, Vector Class, Vector Client Interface, Client Use of Vector, Type-safety in Templates, Grid Class, Grid Client Interface, Client Use of Grid, Stack Class, Stack Client Interface, Queue Class, Queue Client Interface, Client Use of Queue, Nested Templates, Learning a New API, CS106B Library Documentation
Lecture 6 (43:01)
More Containers, Map Class, Uses of Map, Map Client Interface, Live Coding Example: Use of Map, More information on Maps, What s Missing? Iterator Operation Through the Map, Iterating Over the Map, Set Class, Set Client Interface, Live Coding Example : Use of Set, Set Higher-level Operations, Why Set is Different
Lecture 7 (47:32)
Seeing Functions as Data: Specific Plot Functions, Generic Plot Function, Back to the Set, Live Coding Example: Use of Set with User Defined Data Types, Client Callback Function, Review of the Classes Seen,5 Using Nested ADTs (Abstract Data Types), Live Coding Example, Recursion, Recursive Decomposition
Lecture 8 (42:37)
Stumbled Upon: 'I'terator, Common Mistakes Stumbled Upon: Concatenating Strings, Solving Problems Recursively, Functional Recursion, Example of Recursion: Calculating Raise to Power, Demo of "Raise to the Power Example" Through Live Coding, Mechanics of What s Going to Happen in Recursion, More Efficient Recursion, Being Wary of Too Many Base Cases, Recursion & Efficiency, Example: Palindromes, Example: Binary Search, Binary Search Code Walk Through, Choosing a Subset; Choose Code
Lecture 9 (48:04)
Thinking Recursively, Procedural vs Functional Recursion, Fractal Code, Live Demo: Fractal Example, Another Recursive Graphic: Mondrian Art, Random Pseudo-Mondrian and the Code, Hanois Towers : Classic Recursion Example, Tower Code, Live Demo, Permutations, Permute Code, Tree of Recursive Calls
Lecture 10 (47:02)
Refresh: Permute Code, Tree of Recursive Calls, Live Demo: Testing with Different Cases, Eliminating Duplicates, Subsets, Subset Strategy, Subset Code, Tree of RecursiveCalls: Subset, Exhaustive Recursion, Recursive Backtracking, Turning Recursive Permute to Backtracking, Permute -> Anagram Finder Code, Decision Problems: 8 Queens, Extension to N Queens
Lecture 11 (47:48)
Backtracking Pseudocode, Sudoku Solver, Sudoku Code, Cryptarithmetic, Dumb Solver, Smarter Solver, Looking for Patterns, Introduction to Pointers, Single Pointer Operations
Lecture 12 (41:45)
Pointer Movie, Pointer Operations: Code & Pointer Memory Diagrams, Pointer Basics, Pointer and Dynamic Arrays, Use of Pointers, Recursive Data, A Recursive Structure, Live Demo: Working with Linked List, Building the List
Lecture 13 (51:35)
Coding with Linked List, Printing the List, Using Recursion to Print List, De-allocating the Memory Used for the Linked List, Watch the Pointers: Prepend Function, Passing Pointers by Reference, Array vs Linked List, Insert in Sorted (order) Linked List, Insert in Sorted Order: Code, Recursive Insert
Lecture 14 (49:33)
Algorithm Analysis, Evaluating the Performance, Analysis of Codes: Statement Counts, Another Example (Statement Count Contd.), Comparing Algorithm, Big-O Notation, Big-O to Predict the Time of Execution, Best/Worst/Average Case, Analysis of Recursive Algorithms, Another Example : Towers of Hanoi, A Tabulation for Different Algorithms, Growth Patterns, Application of Algorithm Analysis to Sorting, Selection Sort, Selection Sort Code
Lecture 15 (47:20)
Selection Sort, Live Demo: Working/execution of the Code, Selection Sort Analysis, Insertion Sort Algorithm, Live Demo: Working/execution of Insertion Sort, Insertion Sort Analysis, Insertion vs Selection, Quadratic Growth of the Algorithm, Merge Sort, Merge Sort: Working/execution Demo, Merge Sort Code Explanation, Merge Sort Analysis, Quadratic vs Linear Arithmetic, Sort 'Race', Quick Sort Idea
Lecture 16 (47:35)
Partitioning for Quicksort, Quicksort Code Working/execution, Quicksort Code, Live Demo: Running Quicksort vs Merge Sort, Bad Split Example, Worst Case Split, What Input has Worst Case for Quick Sort, Live Demo: Running Quicksort vs Merge Sort, Different Input Scenarios, Strategy to Avoid Worst Case Split, Execution Time Tabulation, Towards Generic Functions: Swap, Function Template, Example Live Code, Template Instantiation and its Errors, Sort Template, Client Use of Sort Template
Lecture 17 (44:31)
Sort Template with Callback, Supplying the Callback Function, One Last Convenience: Default Callback Function, Why Object Oriented Programming, Class Division, Class Interface in ".h" File, Storage for Objects, Accessing Members of a Class, Class Implementation, Implementing Member Functions, Maintaining Object Consistency, Constructors of a Class, Destructors of a Class, Basic Thoughts on Object Design, Internal vs External Representation: Idea of Encapsulation, Better Representation, ADTs (Abstract Data Types)
Lecture 18 (50:54)
Abstract Data Types, Wall of Abstraction, Why ADTs?, Live Coding Example: Creating the Vector Class, Private Data Members, Growing Dynamically: Making Space at Runtime, Insert and Remove Functions, Templatizing the Class Created, Including the "template.cpp" - Why?
Lecture 19 (41:27)
Rules of Template Implementation, Explanation of the Working, Not Allow Member Wise Copy, InsertAt Function, Consequences of Contiguous Memory Being a Disadvantage, Stack Class, The Member Function Definitions, Midterm Post Mortem
Lecture 20 (51:00)
Live Coding: Recap of the Vector-based Implementation for Stack, Linked List Implementation for Stack, Live Coding: Linked List Implementation for Stack, Analyzing Push/pop Functions, Queue Implementation, Live Coding: Queue Implementation, Alternative Implementation, Text Editor Case Study, Buffered Class Interface and Buffer Layered on Vector, Live Coding: Text Editor, Evaluate Vector Buffer, Buffer Layered on Stack, Live Demo, Compare Implementations, Buffer as Linked List
Lecture 21 (46:02)
Buffer: Vector vs Stack, Buffer as Linked List, Cursor Design, Use of Dummy Cell, Linked List Insert/delete, Linked List Cursor Movement, Compare Implementation, Doubly Linked List, Compare Implementation, Space Time Trade Off, Implementing Map, Simple Map Implementation: Vector, Map as Vector : Performance Implication, A Different Strategy
Lecture 22 (49:45)
Map as Vector, A different Strategy: Binary Search Tree, Trees in General, Binary Search Tree for Numbers, Operating on Trees, Tree Traversals at Work, Implementing Map as Tree, Map - getValue(), Important Syntactical Advice, Adding to a BST, Trace treeEnter(), Passing Nodes by Reference, Evaluate Map as a Tree, Impact of the Height of the Tree, Degenerate Trees, What to do About Unbalanced Trees?
Lecture 23 (45:51)
Pathfinder Demo, Graphs: Examples, Graphs: Explanation, Implementation Strategies, Graph Representation in C++, Nodes and Arcs in C++, Graph Traversals, DFS - (Depth First Search), Trace DfS, BFS - (Breadth First Search), Trace BFS, Graph Search Algorithms, Weighted arcs
Lecture 24 (50:19)
Compare Map Implementations, Hashtable Idea, Hash Functions, Hash Collisions, Live Demo: Hashing, Live Coding: Hashing, Hashing Idea : Example in Real World, Hash Table Performance, Compare Map Implementations, Hashing Generic Types, Implementing Set
Lecture 25 (50:36)
Lexicon Case Study, Lexicon as Sorted Vector, Lexicon as BST, Lexicon as Hash Table, Summary so Far, Noticing Patterns/repetitions in the Words, Letter Trie, Lexicon as Trie, Dynamic Array of Children, Flatten Tree into Array, Exploiting Prefixes and Suffixes, DAWG: Directed Acyclic Word Graph, Lexicon as DAWG, The Final Result, Cool Facts about the DAWG
Lecture 26 (49:05)
Final Showdown, Thinking About Design, Runtime Performance, Memory Used, Code Complexity, Making Tradeoffs, Array vs Vector, Stack/Queue vs Vector, Set vs Sorted Vector, Pointer-based vs. Contiguous Memory, CS106B MVPs, Pointers, To Remember Years from Now, After CS106B, considering.cs
Lecture 27 (41:34)
Guest Lecturer: Keith Schwarz, About the C++ Language, Quick History of C++, C++ Philosophy, C++ Without genlib.h, A Working genlib.h Replacement, Other CS106 Headers, strutils.h, simpio.h, random.h, graphics.h/extrgraph.h, What about ADTs?, Standard Template Library, STL Algorithms, Language Features, Operator Overloading, What Next?

Citation

Julie Zelenski, Computer Science II: Programming Abstractions (Stanford University: Stanford Engineering Everywhere), http://see.stanford.edu. License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0

Instructors

Julie Zelenski

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