The schedule below shows the tentative dates for all class topics, readings, and major assignments (MPs and SoLAs). You should complete all assigned readings by 10 p.m. on the night before the class in which they are listed. You should complete all lab writeups before the next class session. You can find the full list of due dates on Gradescope.
Due dates for major assignments will not change. However, the particular topics we cover each day may change as we discover that we need more (or less) time on each topic.
If the title of a class session is blue, you should be able to click on it to view the eboard for that class session.
If you view this page with JavaScript enabled you can jump to the current week on the schedule, and you should see the next day of class highlighted in the schedule below.
We begin the course by exploring some key ideas.
Topics: Course goals. Course structure. Academic honesty. ADTs and data structures. Designing a stack ADT (an exercise). A bit about OOP.
We consider some basic issues of Java programming.
Topics: From C to Java. The structure of a Java program. Compiling and running Java programs. Strings in Java. Numeric types in Java. Arrays in Java. Basic output in Java. Basic input in Java.
We practice designing an ADT and considering its implementation.
Topics: About technical interviews. ADTs and other TLAs. A design exercise.
Because of unexpected matters, we pause to work on the first mini-project.
We consider tools for developing programs in Java, particularly the Microsoft VSCode integrated development environment (IDE) and the Git version control system.
Topics: IDEs. VSCode basics. Version control. Git basics.
We consider Java’s approach to objects, the primary building block of object-oriented programming. We also explore Java’s classes and how one might model them.
Topics: Object basics. Modeling objects with classes. An exercise.
We consider some underlying issues in the design and implementation of objects in Java. We explore some ways to represent objects visually.
Topics: References. The stack and the heap. Representing objects.
We continue considering our continued example of expandable arrays.
We consider interfaces, which serve as specifications of expected behavior for classes. We also explore how interfaces support one form of polymorphism, a key aspect of object-oriented programming.
Topics: Interfaces. The building blocks of OOP. Subtype polymorphism.
We continue our explortation of polymorphism by considering a second type of polymorphism, parametric polymorphism, and its realization in Java’s generics.
Topics: Subtype polymorphism, revisited. Parametric polymorphism. Java generics. Generic classes. Generic interfaces. Generic methods. Generics and arrays.
We return to concepts of unit testing and habits of debugging that you first learned in CSC 151. We also introduce the concept of test-driven development, common among agile developers.
Topics: A few thoughts on testing. An example. Test-driven development. Why use debuggers. Debugging vs. print statements. Debugging in VSCode.
We introduce exceptions and consider their use.
Topics: Exceptions. Object-oriented design.
We consider inheritance, the third core aspect of object-oriented design.
Topics: Inheritance basics. Inheritance and polymorphism. Compile time vs. run time.
We consider lists and ways to think about them. We practice ADT design.
Topics: The design of ADTs, revisited. Scheme lists. Broader concepts of lists. Java lists. Implementing lists.
We consider linear structures, such as queues and stacks. We explore ways in which arrays and simple linked objects can be used to implement linear structures.
Topics: Linear structures. Stacks. Queues. Priority queues. Other linear structures. Implementing linear structures with arrays. Implementing linear structures as linked structures.
We explore ways in which arrays can be used to implement linear structures.
Topics: Detour: Wrappers. Implementing linear structures with arrays. Array-based queues. Priority queues and their implementation.
We consider iterators, a standard mechanism for accessing the elements of a collection. We explore the use of Java’s anonymous inner classes to build iterators.
Topics: Iterators. Iterating array-based structures. Iterating linked linear structures. Named iterators. Anonymous inner classes.
We consider ways to analyze the resource use of algorithms, including formal notation for describing that use.
Topics: Comparing algorithms. Empirical analysis. Asymptotic analysis. Counting steps. Big-O, formalized. Implications of Big-O.
We consider techniques for analyzing recursive algorithms.
Topics: Iterative analysis, revisited. Recurrence relations. Approaches to recurrence relations.
We consider Java’s support for anonymous functions.
Topics: Anonymous functions reviewed. Anonymous functions in Java. Functional interfaces. Priority queues, revisited. Sorted lists.
We take a day to review various topics.
We consider the problem of searching a collection and techniques for searching various kinds of collections.
Topics: Modeling the problem of searching. Sequential search. Predicates. Binary search. Comparators. Testing binary search.
We return to the problem of sorting a list or array.
Topics: The problem of sorting. Testing sorting algorithms. Insertion sort. Selection sort. Generic sorts.
We consider the classic merge sort algorithm.
Topics: Lower bounds on sorting. Divide-and-conquer algorithms. An introduction to merge sort. Analyzing merge sort.
We consider the classic Quicksort algorithm.
Topics: A quick introduction to Quicksort. Partitioning. Partitioning with invariants. Key ideas from Quicksort.
We consider our responsibilities as programming professionals.
Topics: Professional ethics..
We return to the list ADT and explore how to implement lists using arrays.
Topics: A simple list interface. The java.util.List interface. The java.util.ListIterator interace.
We explore more sophisticated versions of the linked-list data structure
Topics: Linked lists, reviewed. Doubly-linked lists. Circularly-linked lists. Other list issues.
We compare the sorting algorithms from the latest mini-projects.
We introduce the tree structure and mechanisms for iterating trees.
Topics: Representing hierarchical information. Tree terminology. Depth-first and breadth-first traversal. Recursive depth-first traversal. Iterative breadth-first traversal. Iterative depth-first traversal. Pre-order, in-order, and post-order traversals.
We consider binary search trees, one of the standard implementations of the Map abstract data type.
Topics: The structure of binary search trees. Organizing binary search trees. Adding elements to BSTs.
We continue our exploration of binary search trees.
Topics: Deletion in BSTs.
We explore one of the two primary collision-resolution mechansims in hash tables.
Topics: Collisions. Linear probing. Quadratic probing.
We consider hash tables, one of the most powerful implementations of the Map abstract data type. We also explore the issue of hash functions.
Topics: Integer maps. From objects to integers. Handling collisions. Rebuilding hash tables. Hash functions.
We explore the second of two primary collision-resolution mechanisms in hash tables.
Topics: Buckets and chaining.
We return to the subject of priority queues and consider heaps, one of the more efficient ways to represent priority queues.
Topics: Priority queues, revisited. The heap structure. Adding elements to heaps. Removing elements from heaps. Storing trees in arrays.
We explore the implementation of heaps.
Topics: Heaps. Heapsort.
We consider a graph abstract data type and some common implementations of graphs.
Topics: Modeling problems with graphs. Graph terminology. Weighted graphs. Directed graphs. Implementing graphs with adjacency matrices. Implementing graphs with adjacency lists. Implementing graphs with edge tables.
We consider the problem of visiting all the nodes in a graph, expanding the approaches we used for trees.
Topics: Review of tree traversal. Breadth-first traversal. Depth-first traversal.
We consider how to build minimum spanning trees in graphs
Topics: Minimum spanning trees. Strategies for building minimum spanning trees. Kruskal’s algorithm. Prim’s algorithm. Greed as an approach to algorithm design.
We consider the problem of finding the shortest path between two nodes in a graph
Topics: The shortest path problem. Shortest paths in unweighted graphs. Shortest paths in weighted graphs. Dijkstra’s algorithm.
We conclude the course.
Topics: The subject matter(s) of the course. Looking ahead. Course evaluation.