Course Description
Course Aims and objectives
Course outlines
Text book and references
Prerequisite
Grading policy
Instructors and office hours
Syllabus
Time table
Lecture Notes
Attached Files
Previous Exams
Course Description
| This course introduces students to data science, teaching essential skills like data analysis, visualization, exploration, supervised and unsupervised machine learning, feature selection, text mining, and evaluation. |
Course Aims And Objective
| • To introduce you to the theoretical foundations of computer science concerning: o the relationships between languages (problems) and machines, o the inherent limits of what can be computed, and o the relative efficiency of problem . • To familiarize you with the applications of theoretical topics to practical problems. • To give you practice creating rigorous arguments using various proof techniques.; |
Course Outline
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| • Introduction and Mathematical Preliminaries - Chapter 1 • Languages, Regular Sets and Expressions - Chapter 2 • Introduction to Grammars - Chapter 3 • Grammar Transformations & Normal Forms - Chapter 4 • Finite Automata - Chapter 5 • Properties of Regular Languages - Chapter 6 • Pushdown Automata & Context-Free Languages - Chapter 7 • Turing Machines - Chapter 8 • The Chomsky Hierarchy - Chapter 10 • Decision Problems & the Church-Turing Thesis - Chapter 11 • Undecidability: the Halting Problem - Chapter 12 • Introduction to Computational Complexity - selected portions of Chapters 14, 15, and 16 |
Text book and references
| The Main Book |
Grading Policy
| Activity | Weight |
Instructors And Office Hours
| The Instructors of the Course |
Miscellaneous
Time Table
| Subject | Hours |
| Attachment Files |
Previous Exams
| Previous Exams |