Artificial Intelligence is an advanced subject, so before starting this subject students should know some basic topics of computer science and mathematics.
AI is not studied alone. It uses ideas from programming, algorithms, mathematics, and statistics. If the student already knows these subjects, then Artificial Intelligence becomes easy to understand.
- Programming – the student should have basic knowledge of programming. The student must know how to write simple programs and how logic works in a program. Any language like C, C++, Java, or Python is enough.
- Data Structures and Algorithms is very important for Artificial Intelligence. Many AI problems use tree, graph, stack, queue, and searching methods. Concepts like recursion and algorithm steps are used again and again in AI.
- Discrete Mathematics – basic knowledge of Discrete Mathematics is required. Artificial Intelligence uses logic in many places. Topics like logic, set, relation, function, and graph theory help in understanding reasoning and knowledge representation.
- Probability and Statistics – students should know Probability and Statistics. In real world problems, we do not always get exact data. Sometimes the result depends on chance. Artificial Intelligence uses probability to handle this type of situation. Machine learning also uses statistics.
- Linear Algebra is also useful in Artificial Intelligence. Some AI methods work with vectors and matrices. Neural network and machine learning models use matrix calculation.
- Operating System and Computer Organization – Basic understanding of both subjects is also good for students, because AI programs sometimes need more memory and more processing.
Artificial Intelligence is a combination of many subjects, so students from computer science, IT, mathematics, electronics, and similar branches can study this subject if their basics are clear.
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Course Objectives
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Course Outcomes
CO1 – Understand the basic concepts of Artificial Intelligence and intelligent agents.
CO2 – Apply search algorithms and problem solving methods to different problems.
CO3 – Explain knowledge representation and reasoning techniques used in AI.
CO4 – Understand the basic idea of machine learning and uncertain reasoning.
CO5 – Identify real world applications where Artificial Intelligence can be used.
