Assam HS Skill Subject Artificial Intelligence study guide
Assam HS Skill Subject Artificial Intelligence · Work through your exam, one topic at a time.
Artificial Intelligence is offered as a skill subject at the higher secondary level in Assam, under the state's vocational and skill education stream for Classes XI and XII. It is designed for students who want a practical, employable grounding in AI rather than a purely theoretical computer science route. The Assam HS Skill Subject Artificial Intelligence study guide has 8 sections and about 2,821 words.
01Introduction to Artificial Intelligence
This domain checks that you can define AI precisely, place ML and DL correctly inside it, recall the historical milestones, describe an intelligent agent's structure, and match AI techniques to real applications instead of to science-fiction robots. Definition: AI is the branch of computer science that builds systems able to do tasks that normally need human intelligence - perception, reasoning, learning, language, and decision-making.
387 words, about 2 min02Python Programming for AI
Python questions here are applied: can you choose the right data type, trace a loop or function, and use NumPy and pandas to load, filter, and summarise a dataset without writing a manual loop? Core types: int, float, str, bool.
361 words, about 2 min03Data Literacy and Data Science Fundamentals
This domain tests whether you can identify data types and scales, describe how data is collected and cleaned, choose an appropriate chart, and interpret descriptive statistics correctly rather than quoting a mean blindly. Structured versus unstructured: tables and spreadsheets are structured; text, images, and audio are unstructured.
341 words, about 2 min04Machine Learning Concepts
You must distinguish supervised from unsupervised learning, separate classification from regression, explain overfitting and the train–test split, and compute model-evaluation metrics from a confusion matrix. Supervised learning uses labelled data.
315 words, about 2 min05Neural Networks and Deep Learning Basics
This domain checks that you can describe a perceptron's computation, explain why activation functions are needed, identify layer types, and outline how training reduces loss through gradient descent and backpropagation. Perceptron: computes z = Σ(wᵢxᵢ) + b, then applies an activation.
337 words, about 2 min06Natural Language Processing and Computer Vision
You must describe a text-processing pipeline, distinguish rule-based, retrieval-based, and generative chatbots, and explain the basics of image classification, including why convolutional networks suit images. Text pipeline: lowercasing and cleaning → tokenisation → stop-word removal → stemming or lemmatisation → vectorisation (bag of words, TF-IDF, embeddings) → model.
358 words, about 2 min07AI Ethics, Bias, Privacy and Societal Impact
This domain asks you to apply responsible-AI principles to concrete cases, identify sources of bias, explain data-protection obligations, and discuss how automation changes work rather than repeating slogans. Responsible AI principles: fairness, transparency and explainability, accountability, privacy, safety and robustness, and human oversight.
368 words, about 2 min08AI Project Lifecycle and Capstone Project
This domain tests project discipline: framing a problem so it is measurable and ethical, preparing a dataset without leakage, selecting and evaluating a model, and documenting and presenting the result for a non-technical audience. Lifecycle phases (CRISP-DM style): business/problem understanding → data understanding → data preparation → modelling → evaluation → deployment, with iteration back to earlier phases.
354 words, about 2 min