Basic Information

Course Code:

CS 3001

Course Name:

Algorithms

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

5.00

Instructor:

Prof. Dr. Hilal KAZAN

Course Objectives

The main objective of this course is to provide the students with a knowledge on foundations of problem solving, computational efficiency, and experience in the design and implementation of algorithms commonly employed in computer science and computational problems.

Course Content

Introduction to the main concepts of design and analysis of algorithms. Overview of basic analysis techniques: approximating functions asymptotically, bounding sums, and solving recurrences. Discussion of efficiently solvable problems with a focus on design techniques such as divide-and-conquer, randomization, dynamic programming, amortization, and greedy algorithms. Illustration of various new concepts through algorithms applied to problems related to sets, sequences, strings, graphs etc.

Prerequisites / Corequisites

CS 1002 and CS 2007

Learning Outcomes

Code	Description
LO1	Explains the working principle of iteration and uses it in problem solving.
LO2	Explains the logic behind how different sorting algorithms work.
LO3	Analyzes the worst-case, average-case, and best-case time complexities of an algorithm.
LO4	Expresses the time and space complexity of an algorithm using asymptotic notation.
LO5	Solves problems using divide-and-conquer, greedy, and dynamic programming techniques, and compares these techniques.

Weekly Course Content

Week	Content
1	Introduction and course Logistics
2	Divde and conquer, merge sort
3	Asymptotic analysis, Running time analysis of merge sort
4	Asymptotic analysis, Running time analysis of merge sort
5	Masters Theorem
6	Heapsort
7	Quicksort
8	Midterm
9	Quicksort and Selection Algorithm
10	Dynamic Programming (DP) (Rod Cutting)
11	Dynamic Programming (DP) (Longest Common Subsequence)
12	Greedy Algorithms (Activity Selection, Knapsack problems)
13	Greedy Algorithms (Huffman encoding)
14	Comparison of DP and Greedy algorithms
15	Review

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	14	3.00	42.00
Pre-Class Individual Study	14	1.50	21.00
Midterm Exam/Preparation	1	15.00	15.00
Final Exam/Preparation	1	18.00	18.00
Quiz Preparation	3	10.00	30.00
Homework	3	8.00	24.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Final Exam	%35
2	Midterm Exam	%30
3	Quiz	%25
4	Homework	%10
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2	Homework	Solving and analyzing homework questions using a computer and Excel.	Computer

Sustainable Development Goals