RTU Kota B.Tech 6th Semester Machine Learning Question Paper 2022 (IT)

RTU Machine Learning 2022 Paper Review

The Machine Learning course for the IT branch at Rajasthan Technical University serves as the bridge between statistical theory and practical system automation. The 2022 examination focused on the core mechanics of learning algorithms and the mathematical intuition required to tune models for better predictive performance. For IT students, this subject is essential for understanding how to handle large datasets, optimize model accuracy, and implement supervised and unsupervised learning techniques.

This review breaks down the 2022 paper structure to help you understand the examiners' priorities and refine your preparation strategy for current assessments.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks, organized into three parts:

• Part A: Ten compulsory questions, two marks each. Focus on definitions like "Machine Learning," "Supervised vs. Unsupervised Learning," "Bias-Variance Tradeoff," and the purpose of activation functions in neural networks. Keep answers concise—under 30 words.
• Part B: Seven questions; answer five. Each is worth four marks. These are analytical. Expect questions on the mechanics of a decision tree split, differences between K-means and Hierarchical clustering, and the evaluation metrics like Precision, Recall, and F1-score.
• Part C: Five major questions; answer three. Each is worth ten marks. These require detailed technical explanations or calculations. Prepare for problems on Naive Bayes classification, solving linear regression equations, tracing the K-nearest neighbor algorithm, or detailing the backpropagation process.

Core Topics Evaluated in the 2022 Paper

Focus your study time on these specific modules to maximize your score:

Supervised Learning

This is the most critical module. You must master the math behind Linear Regression and Logistic Regression. For classification, practice the Naive Bayes theorem, as examiners often provide a small dataset and ask you to calculate the posterior probabilities. Pay special attention to the Decision Tree induction process—specifically how Information Gain and Entropy are calculated.

Unsupervised Learning

Understand the logic of grouping unlabelled data. Master the K-means algorithm—you should be able to manually trace the steps of centroid initialization and cluster assignment. Study hierarchical clustering and association rule mining, which frequently appear in Part C.

Performance Evaluation

You must know how to evaluate your models. Understand the confusion matrix and be able to calculate Accuracy, Precision, Recall, and F1-score from a given set of true positive/negative and false positive/negative results.

Statistical Learning and Neural Networks

Understand the bias-variance tradeoff, as this is a fundamental concept in model selection. For neural networks, study the perceptron model and the basics of how backpropagation updates weights to minimize error.

Answer Writing Strategy for High Marks

RTU evaluators look for logical progression and clear, structured technical content.

• Diagrams: Use a ruler to draw flowcharts for algorithms. For Decision Trees, clearly draw the nodes and branches. For K-means, draw simple scatter plots showing clusters.
• Formatting: Use a black pen for algorithm names, formulas, and diagrams. Use a blue pen for your explanatory text. Use bullet points for features, advantages, and limitations to make your answers scannable.
• Precision: If the question asks for an algorithm, define the objective, the mathematical basis, and provide a step-by-step procedure. When calculating probabilities or weights, show your intermediate arithmetic steps clearly.
• Comparative Tables: Whenever the paper asks to compare two concepts—like "Supervised vs. Unsupervised learning" or "K-nearest neighbor vs. Decision Tree"—always use a table to clearly delineate their differences.

Time Management During the Exam

• Part A (20 minutes): Answer these first. Aim for short, punchy definitions that hit the key technical keywords.
• Part B (40 minutes): Limit each answer to 8 minutes. Focus on drawing the required diagrams early so you have time to explain them.
• Part C (120 minutes): Devote 40 minutes per major question. Use this time to write out full logic proofs and detailed algorithmic traces without rushing. If a question asks for an algorithm, define it, state the steps, provide a small example or diagram, and mention a real-world use case.