Mathematics

Algorithms and Advanced Programming I(MAT231)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
MAT231 Algorithms and Advanced Programming I 3 3 0 0 3 5
Prerequisites
Admission Requirements
Language of Instruction French
Course Type Compulsory
Course Level Bachelor Degree
Course Instructor(s) MUHAMMED ULUDAĞ muhammed.uludag@gmail.com (Email)
Assistant
Objective The purpose of this course is to improve students programming capabilities by the study of some common algorithms, their implementations and their applications to sample computational problems.
Content Programming review (with Python): variables and state, conditionals, loops, functions
Basic data structures: list, multi dimensional array, tree
Common algorithms: search, sort, aggregate functions
Recursion: numeric computation, tree traversal (inorder/preorder/postorder)
Algorithm Analysis: time/space complexity classes
Course Learning Outcomes When given a problem, students will be able to identify logical parts of the problem, their interdependencies and algorithms which can be used to solve each.
When given an algorithm, students will be able to compare its performance and characteristics to other algorithms of the same class.
When given a source code, students will be able to identify data structures and algorithms that are implemented in the given source code.
Teaching and Learning Methods lecture, programming home work
References The Art of Computer Programming - Donald Knuth
Python - How to Program - Deitel
Data Structures and Algorithms Using Python - Rance D. Necaise
Data Structures and Algorithms with Object-Oriented Design Patterns in Python - Bruno R.Preiss
Print the course contents
Theory Topics
Week Weekly Contents
1 Programming review: value, expression, variable, data type, assignment, program state, enumerating loops
2 Programming review: conditionals, execution branching, conditional loops, nested loops and conditionals
3 Programming review: functions, parameters, return value, code flow, stack frames, variable scope
4 Sequences, patterns, multidimensional patterns from loop indices, data dependence
5 Implementing aggregate functions: min, max, sum, count, avg, std.dev, unique
6 Sorting values on a list: naive approach, insertion sort, bubble sort, merge sort
7 Midterm I
8 Recursion: depth bounding, flow of function calls, examples: factorial, fibonacci, quick sort
9 Trees: depth first, breadth first traversal, in-order/pre-order/post-order traversal
10 Stack, Queue, relation of stack with recursion, recursion removal
11 Midterm II
12 Numerical algorithms: random number generation, root finding, linear regression
13 Search: simple search, binary search, searching recursively
14 Time/space complexity, Complexity classes, comparison of algorithms
Practice Topics
Week Weekly Contents
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 2 40
Contribution of final exam to overall grade 1 60
Toplam 3 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 2 40
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 2 40
No Program Learning Outcomes Contribution
1 2 3 4 5
1 understands principles of deductive reasoning; has experience to verify well-foundedness and exactness of mathematical statements in systematic ways; X
2 can properly state and use concepts and results of major mathematical interest; X
3 masters current computational techniques and algorithms; has a good ability in their use; can identify relevant tools, among those one has learned, suitable to solve a problem and is able to judge whether or not one is in possession of these tools; X
4 is able to express one’s mathematical ideas in an organised way both in written and oral forms; X
5 understands relations connecting substantial concepts and results; can switch from one viewpoint to another on mathematical objects (pictures, formulae, precise statements, heuristic trials, list of examples,...); X
6 has followed individually a guided learning strategy; has pursued steps toward the resolution of unfamiliar problems; X
7 has a theoretical and practical knowledge in computer science well adapted for learning a programming language; X
8 has investigated the relevance of modeling and using mathematical tools in natural sciences and in the professional life; is conscious about historical development of mathematical notions; X
9 has followed introduction to some mathematical or non-mathematical disciplines after one’s proper choice; had experience to learn selected subjects according to one’s proper arrangement; X
10 masters French language as well as other foreign languages, to a level sufficient to study or work abroad. X
Activities Number Period Total Workload
Class Hours 14 3 42
Working Hours out of Class 14 2 28
Assignments 14 1 14
Presentation 0 0 0
Midterm Examinations (including preparation) 1 10 10
Project 0 0 0
Laboratory 0 0 0
Other Applications 0 0 0
Final Examinations (including preparation) 1 20 20
Quiz 0 0 0
Term Paper/ Project 0 0 0
Portfolio Study 0 0 0
Reports 0 0 0
Learning Diary 0 0 0
Thesis/ Project 0 0 0
Seminar 0 0 0
Other 0 0 0
Total Workload 114
Total Workload / 25 4,56
Credits ECTS 5
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