Syllabus

Week 1

5/04:

Lecture 1: Data Definitions in Java Designing Data: Simple classes, classes with containment Lecture 2: Data Definitions: Unions Designing unions of classes; self-reference

5/05:

Lecture 3: Methods for simple classes Methods for simple classes and classes with containment Lecture 4: Methods for unions Methods for unions of classes and classes with self-reference

5/06:

Lecture 4: Methods for unions Methods for unions of classes and classes with self-reference Lecture 5: Methods for self-referential lists Designing methods for complex class hierarchies

5/07:

Lecture 5: Methods for self-referential lists Designing methods for complex class hierarchies Lecture 6: Accumulator methods More complicated methods for self-referential data

Lab:

Lab 1 Data definitions in Java; Eclipse IDE; Handins homework server Lab 2 Designing methods in Java: classes, containment, unions, self-reference

Week 2

5/11:

Lecture 6: Accumulator methods More complicated methods for self-referential data Lecture 7: Accumulator methods, continued (continued) Methods with accumulators

5/12:

Lecture 7: Accumulator methods, continued (continued) Methods with accumulators Lecture 8: Practice Design Recap: designing methods and wish lists for a larger problem

5/13:

Lecture 9: Abstract classes and inheritance Abstract classes and inheritance Lecture 10: Customizing constructors for correctness and convenience Customizing constructors for correctness and convenience

5/14:

Lecture 10: Customizing constructors for correctness and convenience Customizing constructors for correctness and convenience Lecture 11: Defining sameness for complex data, part 1 Sameness of data values

Lab:

Lab 3 Designing methods for self-referential data Lab 4 Practice with abstract classes and constructors

Week 3

5/18:

Lecture 11: Defining sameness for complex data, part 1 Sameness of data values Lecture 12: Defining sameness for complex data, part 2 More of the same

5/19:

Lecture 13: Abstracting over behavior Overview of world programming; Function objects Lecture 14: Abstractions over more than one argument More function objects

5/20:

Lecture 14: Abstractions over more than one argument More function objects Lecture 15: Abstracting over types Generics

5/21:

Exam 1

Lab:

Lab 5 Functional world games No lab

Exam 1: online

Week 4

5/25:

No class: Memorial Day

5/26:

Lecture 16: Visitors Visitors

5/27:

Lecture 17: Mutation Creating cyclic data, mutation Lecture 18: Mutation inside structures Testing mutation methods, indirect cycles

5/28:

Lecture 19: Mutation, aliasing and testing Aliasing and equality, revisited

Lab:

Lab 6 Working with function objects Lab 7 Generics and Visitors

Week 5

6/01:

Lecture 19: Mutation, aliasing and testing Aliasing and equality, revisited Lecture 20: Mutable data structures Removing items from lists, mutable lists

6/02:

Lecture 21: ArrayLists Direct-access data structures: ArrayList; swapping two items, mapping over ArrayLists, for-each loops Lecture 22: ArrayLists ArrayLists and binary search; for-each loops; working with indices

6/03:

Lecture 22: ArrayLists ArrayLists and binary search; for-each loops; working with indices Lecture 23: For-each loops and Counted-for loops For-each loops and counted-for loops

6/04:

Lecture 24: While loops While-loops Lecture 25: Iterator and Iterable Iterators and Iterables

Lab:

Lab 8 Working with mutable and cyclic data Lab 9 Loyd's 15 puzzle: working with ArrayLists, loops and imperative worlds

Week 6

6/08:

Lecture 26: Hashing and Equality HashMap, equals

6/09:

Lecture 26: Hashing and Equality HashMap, equals Lecture 27: Introduction to Big-O Analysis Big Oh: searching and sorting; insertion sort and selection sort

6/10:

Lecture 28: Quicksort and Mergesort Big Oh: searching and sorting; quicksort and merge sort Lecture 29: Priority Queues and Heapsort Big Oh: searching and sorting; HeapSort -- Priority Queue

6/11:

Exam 2

Lab:

Exam review

Exam 2: online

Week 7

6/15:

Lecture 28: Quicksort and Mergesort Big Oh: searching and sorting; quicksort and merge sort

6/16:

Lecture 29: Priority Queues and Heapsort Big Oh: searching and sorting; HeapSort -- Priority Queue Lecture 30: Breadth-first search and Depth-first search on graphs Graph algorithms: breadth-first search, depth-first search

6/17:

Lecture 31: Dijkstra’s Algorithm for single-source shortest paths Graph algorithms: Dijkstra's algorithm, and comparison with BFS/DFS Lecture 32: Minimum Spanning Trees Minimum spanning trees: Prim's and Kruskal's algorithms

6/18:

Lecture 34: Implementing Objects Implementing Objects

Lab:

Lab 11 Heapsort, stress tests Lab 12 TBD