An artificial intelligence syllabus usually covers six areas: math and programming foundations, problem solving and search, knowledge representation and reasoning, machine learning and deep learning, applied AI (NLP, computer vision, generative AI) and AI ethics. Below you’ll find the core AI subjects, a semester-wise B.Tech AI & ML syllabus, PG and certificate syllabi, and a job-ready module plan for 2026. Use it as an AI syllabus checklist, whether you’re choosing a degree, a course or what to study next.<\/p>\n\n\n\n
Artificial Intelligence (AI) is a branch of computer science focused on creating systems capable of reasoning, learning, and performing tasks that traditionally require human intelligence. Modern AI spans classical machine learning, deep learning, generative AI and agentic AI.<\/p>\n\n\n\n
Types of AI:<\/strong> by capability, AI is grouped into narrow, general and super AI. By functionality, it’s grouped into reactive machines, limited memory, theory of mind and self-aware AI. Everything you can actually use today, from ChatGPT to your bank’s fraud filter, is narrow AI.<\/p>\n\n\n\n Whatever the program, the same core AI subjects keep showing up. A B.Tech or BSc in AI typically has 8\u201312 core AI subjects spread across semesters, plus math, programming and electives. Short certificates pack the applied half of this list into 5\u20138 modules.<\/p>\n\n\n\n [IMAGE: Hub-and-spoke infographic. Centre “AI Syllabus”. Spokes: Foundations (Math, Python) \u00b7 Search & Problem Solving \u00b7 Knowledge Representation \u00b7 Uncertainty & Bayesian Networks \u00b7 Machine Learning \u00b7 Deep Learning \u00b7 NLP \u00b7 Computer Vision \u00b7 Generative AI \u00b7 AI Ethics. Alt text: artificial intelligence syllabus core subjects. File: artificial-intelligence-syllabus-subjects.webp]<\/p>\n\n\n\n<\/span>Artificial Intelligence Syllabus at a Glance: Core Subjects<\/strong><\/span><\/h2>\n\n\n\n