| Course Code |
CSC354 |
| Course Title |
Design & Analysis of Algorithms |
| Credit Hours |
3+0 |
| Prerequisites by Course(s) and Topics |
Course : Data Structure & Algorithms Topics : Complexity Analysis; Arrays; Sorting Algorithms; Linked Lists; Queues; Recursion; Tree Traversal; Graphs Representation & Traversal. |
| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
|
| Course Coordinator |
Adeel Munawar |
| URL (if any) |
|
| Current Catalog Description |
● Introduction to Algorithms, T. H. Cormen, C. E. Leiserson, and R. L.Rivest, MIT Press, McGraw-Hill, 3rd Edition, New York, NY, 2010. |
| Textbook (or Laboratory Manual for Laboratory Courses) |
|
| Reference Material |
● Algorithms in C++; Robert Sedgewick |
| Course Goals |
• Explain what is meant by “best”, “expected”, and “worst” case behavior of an algorithm • Identify the characteristics of data and/or other conditions or assumptions that lead to different behaviors. • Determine informally the time and space complexity of simple algorithms • List and contrast standard complexity classes • Use big O, Omega, Theta notation formally to give asymptotic upper bounds on time and space complexity of algorithms • Use of the strategies(brute-force, greedy, divide-andconquer, and dynamic programming) to solve an appropriate problem • Solve problems using graph algorithms, including singlesource and all-pairs shortest paths, and at least one minimum spanning tree algorithm • Trace and/or implement a string-matching algorithm |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
|
|
|
| Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures) |
| Week | Lecture | Topics Covered |
|---|
| Week 1 |
1 |
Introduction to Algorithm |
|
2 |
Structured description of algorithm, Three Examples of Algorithms |
| Week 2 |
3 |
Algorithm Analysis (Algorithm Correctness) |
|
4 |
Algorithm Analysis (Algorithm Efficiency) |
| Week 3 |
5 |
Growth of Functions: Asymptotic notation and examples of Big Oh, Big Omega, and Theta. |
|
6 |
Sorting Problem: Insertion Sort |
| Week 4 |
7 |
Analysis of Insertion Sort, Loop invariants of Insertion Sort. |
|
8 |
Divide-and-Conquer Approach: Merge Sort: Example, Complete Working |
| Week 5 |
9 |
Quick Sort: Example, Complete Working |
|
10 |
Heap Sort: Heaps, Maintaining the Heap property |
| Week 6 |
11 |
Heap Sort: Building a Heap, The Heap Sort algorithm Sorting in Sorting in Linear Time: Radix sort |
|
12 |
Linear Time: Bucket sort Sorting in Linear Time: Counting sort |
| Week 7 |
13 |
The substitution method for solving recurrences, The master method for solving recurrences |
|
14 |
The recursion-tree method for solving recurrences |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Dynamic Programming Element of the dynamic programming |
|
16 |
Matrix Chain Multiplication |
| Week 10 |
17 |
Greedy Algorithms Activity Selection Problem |
|
18 |
Element of the greedy strategy |
| Week 11 |
19 |
Greedy Vs Dynamic programming Knapsack Problem |
|
20 |
Huffman Trees and Codes |
| Week 12 |
21 |
Huffman Trees and Codes |
|
22 |
Elementary Graph Algorithms Representation of graph |
| Week 13 |
23 |
Breadth-First Search Depth-First Search |
|
24 |
Topological Sort |
| Week 14 |
25 |
Minimum Spanning Tree |
|
26 |
Growing a Minimum Spanning Tree |
| Week 15 |
27 |
Kruskal’s Algorithm Example |
|
28 |
Prim’s Algorithm Example |
| Week 16 |
29 |
Single-Source Shortest Paths |
|
30 |
Single-Source Shortest Paths in DAG Dijkstra’s Algorithm |
| Week 17 |
2 hours |
Final Term |
|
| Laboratory Projects/Experiments Done in the Course |
|
| Programming Assignments Done in the Course |
|
