Guide · AIML for students

CSE-AIML Syllabus & Subjects, Mapped to Free Resources

Updated October 2026

The CSE-AIML syllabus is the normal CSE core (programming, data structures, DBMS, OS, networks) plus AI subjects: maths for ML, probability & statistics, machine learning, deep learning, NLP and computer vision. Below are the typical subjects for each semester, taken from real university schemes, and for every AI/ML subject the free course we would actually use to learn it.

01AIML subjects at a glance

Every university writes its own scheme, so subject names, order and electives change from college to college. We compared the published CSE (AI & ML) schemes of JNTU Hyderabad (R22), an AKTU-affiliated autonomous college (KIET, 2024–28 batch), a VTU-affiliated autonomous college (Global Academy of Technology, 2022 scheme) and a programme built on the AICTE model curriculum (DDU Gorakhpur University, 2025–26). They all follow the same shape:

Block	Typical subjects	Share of the degree
Science & maths	Calculus, linear algebra, discrete maths, probability & statistics, physics/chemistry	Mostly 1st and 2nd year
CSE core	Programming (C/Python/Java), data structures, algorithms, DBMS, OS, computer networks, computer organisation, automata/compilers	Biggest block, 2nd and 3rd year
AI/ML core	Intro to AI, machine learning, deep learning, NLP, data analytics/data mining	From 2nd-year 2nd semester onward
Electives	Computer vision, image processing, reinforcement learning, big data, cloud, IoT, conversational AI	3rd and 4th year
Projects	Mini project, internship, major project	Mostly 3rd and 4th year

02CSE-AIML syllabus semester-wise (typical)

This is a composite of the schemes above. Treat it as a map, not as your official syllabus: always check your own university's scheme PDF for exact subject names and credits.

Semester	Typical subjects
Sem 1	Calculus / matrices, physics or chemistry, programming for problem solving (C or Python), basic electrical, English, engineering graphics/workshop
Sem 2	Differential equations & vector calculus, Python programming, chemistry or physics, basic electronics, IT workshop
Sem 3	Data structures, discrete maths, computer organisation & architecture, OOP (Java/C++), mathematical & statistical foundations
Sem 4	Probability & statistics / linear algebra, DBMS, operating systems, design & analysis of algorithms, intro to AI or ML essentials
Sem 5	Machine learning (+ lab), computer networks, automata theory, data analytics, elective I (e.g. data science, image processing)
Sem 6	Deep learning or neural networks, NLP (+ lab), data mining / warehousing, knowledge representation, elective II (e.g. computer vision, pattern recognition), mini project
Sem 7	Advanced electives (reinforcement learning, big data, cloud, generative AI), project stage I, industrial training
Sem 8	Electives (e.g. federated ML, conversational AI, speech processing), major project, internship

AIML subjects in 1st year

The 1st year is almost the same as any other engineering branch: maths, physics or chemistry, basic electrical or electronics, and one or two programming courses. Some programmes add an early AI subject (DDU Gorakhpur's AICTE-based scheme lists “Mathematics for Machine Learning” and “Conversational AI”), but most leave AI for the 2nd year. The best use of the 1st year is to get very comfortable with Python and the maths you are already studying.

03Free resources for every core AIML subject

College lectures cover the exam. These free courses cover the understanding. Most of them are also linked topic by topic, with progress tracking, inside our Machine Learning roadmap and AI Engineer roadmap.

Subject	Best free resource	Also good
Linear algebra (maths for ML)	MIT OCW 18.06 (Gilbert Strang)	3Blue1Brown: Essence of Linear Algebra
Calculus & maths for ML	Mathematics for Machine Learning (free book)	3Blue1Brown: Essence of Calculus
Probability & statistics	StatQuest (Josh Starmer)	Khan Academy: Statistics & Probability
Python programming	Harvard CS50P	The official Python tutorial
Data structures & algorithms	MIT OCW 6.006 Introduction to Algorithms	Princeton Algorithms Part I (free to audit)
DBMS & SQL	Harvard CS50 SQL	CMU 15-445 Database Systems
Artificial intelligence (search, logic, planning)	Harvard CS50 AI	Berkeley CS188
Machine learning	Stanford CS229 (Andrew Ng)	Google ML Crash Course
Deep learning	fast.ai: Practical Deep Learning	Dive into Deep Learning (d2l.ai) and Karpathy: Neural Networks Zero to Hero
NLP	Hugging Face LLM (NLP) Course	Stanford CS224N
Computer vision / image processing	Stanford CS231n notes	Hugging Face Computer Vision Course
Data mining & analytics	Mining of Massive Datasets (Stanford, free book + lectures)	Kaggle Learn: Feature Engineering
Reinforcement learning	David Silver's RL course (UCL/DeepMind)	Hugging Face Deep RL Course and Sutton & Barto (free book)
Big data	DataTalksClub Data Engineering Zoomcamp	Apache Spark quick start
Generative AI & LLMs	Karpathy: Let's build GPT from scratch	Our AI Engineer roadmap (LLM APIs, RAG, agents, evals)

Prefer Hindi explanations? The Machine Learning roadmap also lists Hindi playlists (such as CampusX) next to the English ones for the maths, ML and deep learning topics.

04Subjects your syllabus covers lightly (but jobs expect)

University schemes change slowly, and AI work has changed quickly. In the schemes we checked, these skills were missing or appeared only as an elective, yet they show up in almost every AI/ML job post:

Git, Linux and Docker: how real code is shared and shipped. Covered in the software engineering section of the AI Engineer roadmap.
PyTorch: most labs teach scikit-learn and maybe Keras. PyTorch is the default for deep learning work. See PyTorch vs TensorFlow.
LLM APIs, RAG and agents: rarely in the syllabus before the final-year electives, and the fastest-growing area. The AI Engineer roadmap covers it end to end.
Deploying a model: wrapping it in a FastAPI service and putting it online. Labs almost never ask for this; recruiters notice it.
Projects: a lab record is not a portfolio. Pick something from our AIML project ideas.

05How to study the syllabus without falling behind

Learn each subject one semester early. Do linear algebra and Python in the 1st year, and ML basics in the 2nd-year summer, so the 3rd-year ML subject becomes revision.
Use college for marks, free courses for depth. Write exam answers from your university notes; build understanding from the resources above.
Turn every lab into a GitHub repo. Clean it up, add a README, and you have the start of a portfolio.
Follow one plan. The AIML roadmap for BTech students lays out what to learn and build each year alongside this syllabus.

06Frequently asked questions

What are the AIML subjects in 1st year?

Mostly the common engineering 1st year: calculus and linear algebra, physics or chemistry, basic electrical or electronics, English, and programming in C and/or Python. A few universities add an introductory AI or maths-for-ML course.

In which semester does machine learning start in CSE-AIML?

In most schemes, machine learning is a core subject in the 4th or 5th semester (2nd-year end or 3rd-year start), usually after probability & statistics and an introduction to AI.

Is the CSE-AIML syllabus different from CSE?

The first two years are nearly identical. From the 2nd or 3rd year, CSE-AIML replaces some general electives with ML, deep learning, NLP, data analytics and computer vision. The CSE core (DSA, DBMS, OS, networks) stays.

Does the AIML syllabus have a lot of maths?

More than plain CSE: linear algebra, calculus, probability and statistics, and discrete maths. You need to understand it well enough to follow how models learn, not to prove theorems. The free resources above are the friendliest way in.

Is the university syllabus enough to get an AI/ML job?

Usually not on its own. It gives you the theory, but jobs also expect PyTorch, Git, deployment, LLM tools and real projects. Use the syllabus as the base and add projects and a roadmap on top.

Sources

Start the free AIML roadmap