NOTE: The Final Exam is cumulative, but weighted towards material covered after the Midterm.

You are responsible for material covered in:

• Lectures
• Readings
• Homeworks
• Programs

A selection of sample exam questions is available here: Final Practice problems

Midterm Exam:

Topics

• General
  abstract data type (ADT), data structure, Java interfaces (defining, implementing, using an interface as a type), using Java generics, primitive vs. reference types in Java
• Lists
  List ADT, List operations, implementing List using an array, implementing List using a linked list (i.e., a chain of linked nodes), complexities of List operations for different implementations, using a List
• Iterators
  what they are, how to use them, how to implement one for an ADT when the underlying data structure is an array or a chain of linked nodes
• Java Exceptions
  defining new exceptions, checked vs. unchecked, throwing an exceptions using throw, handling an exception using try-catch, handling an exception by passing it down the call stack, listing exceptions in a throws clause
• Complexity
  what complexity is, problem size, constant time (O(1)), linear time (O(N)), quadratic time (O(N²)), big-O notation, worst-case vs. best/average cases, determining complexity of Java code, limitations of complexity analysis
• Linked Lists (i.e., Chains of Linked Nodes)
  list node, linked list operations, head pointer, tail pointer, dummy header node, singly linked lists, doubly linked lists, circular linked lists, comparison of linked list variations

Final Exam:

Topics

• Stacks
  Stack ADT, Stack operations, implementing Stack using an array, implementing Stack using linked list variations, complexities of Stack operations for different implementations, using a Stack
• Queues
  Queue ADT, Queue operations, implementing Queue using array variations, circular arrays, implementing Queue using linked list variations, complexities of Queue operations for different implementations, using a Queue
• Recursion
  recursive methods, base case, recursion rules, how method calls work (i.e., activation records and the call stack), recursion vs. iteration, recursive data structures, analyzing complexity of recursive methods, recurrence equations, determining complexity using recurrence relations
• Search
  Java interfaces (defining, implementing, using an interface as a type), the Comparable interface, sequential search, binary search, complexities of search algorithms
• Trees
  position-oriented vs. value-oriented ADTs, tree terminology (root, leaf, node, edge, parent, child, sibling, ancestor, descendant, subtree, path, length of a path, height of a tree, full tree, complete tree, height-balanced tree), general trees, binary trees, implementation of trees, writing recursive methods that traverse a tree, pre-order traversal, post-order traversal, level-order traversal, in-order traversal
• Binary Search Trees
  binary search trees (BSTs), key value, implementing BSTs, algorithms for BST operations (print, lookup, insert, delete), in-order successor, in-order predecessor, complexities of BST operations
• Red-Black Trees
  balanced search trees, red-black trees, red-black tree properties, root property, red property, black property, algorithms for red-black tree operations (print, lookup, insert)
• Priority queues
  Priority Queue ADT, implementing Priority Queue (using a heap, BST, unsorted array, sorted linked list, etc.) and implications for complexities, heap operations (algorithms & complexities), array implementation of heaps
• Hashing
  hashing terminology: key, hash function, hash code/index, hash table, load factor, collision, ideal hashing; hash table operations (algorithms & complexities); choosing table size; defining a good hash function, hashing techniques: mod by table size, folding, extraction, weighting, handling non-integer keys; collision handling strategies: open addressing (linear & quadratic probing, double hashing) and buckets (using different data structures)
• Graphs
  graph terminology: node, vertex, edge, directed, undirected, source, target, adjacent, successor, predecessor, degree, in-degree, out-degree, path; classifying different kinds of graphs: cyclic, acyclic, complete, connected, weakly-/strongly-connected, connected component, weighted, DAG; graph ADT (implementations & complexities), representing edges: adjacency lists, adjacency matrix; graph traversals (BFS & DFS), spanning trees, finding paths & cycles, Dijkstra's algorithm, topological ordering
• Sorting
  comparison sorts: (algorithms & complexities - time & space, best & worst case circumstances) including bubble sort, insertion sort, selection sort, merge sort, quick sort, heap sort; stable sort, radix sort