RTU Kota B.Tech 6th Semester Machine Learning Question Paper 2025 (CSE/IT/AI)

RTU Machine Learning 2025 Paper Review

Preparing for the Rajasthan Technical University B.Tech Machine Learning exam requires a strict understanding of statistical theory, optimization algorithms, and probabilistic models. For developers building full-stack applications in MERN or Next.js, this theoretical foundation dictates how you integrate intelligent features like product recommenders or predictive analytics into production environments. You cannot reliably deploy a sentiment analysis model without understanding how mathematical weights update during training.

The 2025 paper tests your capability to execute principal component analysis, trace decision tree splits, and calculate weight updates using backpropagation. Publishing this specific 6th-semester paper review directly to exam-support.in provides engineering students exactly what they need to understand how examiners construct algorithmic problems and distribute marks across the mathematical modules. This targeted preparation strategy helps approach the exam confidently, Jaiprakash.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks. The paper features three distinct sections designed to evaluate both basic algorithmic definitions and complex mathematical executions.

• Part A: This section contains ten compulsory questions worth two marks each. You must state the difference between supervised and unsupervised learning, define the Bellman equation, list the steps of K-means clustering, or define support vectors under 30 words.
• Part B: You will find seven questions here. You must answer five of them. Each question is worth four marks. Your answers require explaining the Naive Bayes probability formula, tracing a Q-Learning update step, or differentiating between Monte Carlo and Temporal Difference learning.
• Part C: This section offers five major questions. You need to answer three. Each question carries ten marks. These require you to perform feature extraction using Singular Value Decomposition (SVD), calculate the information gain for a given dataset to construct a Decision Tree, or trace a complete forward and backward pass for a Multilayer Perceptron.

Core Topics Evaluated in the Paper

The 2025 question paper covers several critical modules that establish the mathematical rules for intelligent systems. Focus your study time on these specific areas to maximize your score.

Supervised Learning Algorithms

This module evaluates your understanding of predicting labeled target variables. You must master the mathematics behind Linear Regression, Logistic Regression, and Support Vector Machines (SVM). Practice calculating Gini impurity and Information Gain for Decision Trees. The paper heavily features numerical problems where you must trace the optimal hyperplane margin for an SVM or compute probabilities using the Naive Bayes classifier.

Unsupervised Learning and Association Rules

Grouping unlabeled data requires geometric and probabilistic logic. You must understand how to execute the K-means and Hierarchical clustering algorithms step-by-step. For market basket analysis, study Association rule mining, specifically the Apriori and FP-growth algorithms. Practice calculating Support, Confidence, and Lift for a given transaction database.

Statistical Learning and Feature Extraction

High-dimensional data requires mathematical reduction before training. You must master Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Study how to calculate the covariance matrix, extract eigenvectors, and determine the eigenvalues to identify the principal components. Review filter, wrapper, and embedded methods for feature subset selection.

Reinforcement Learning

This module focuses on state-action reward mechanisms. You must understand the Markov Decision Process (MDP) and memorize the Bellman equations for policy evaluation. Practice tracing the mathematical updates for Q-Learning and SARSA algorithms.

$$Q(s,a) \leftarrow Q(s,a) + \alpha \left[ r + \gamma \max_{a'} Q(s',a') - Q(s,a) \right]$$

Artificial Neural Networks and Deep Learning

You must understand how artificial neurons compute non-linear boundaries. Study the Perceptron learning rule and Multilayer networks. The most heavily weighted calculation in this section is Backpropagation. You must know how to apply the chain rule to update weights based on the calculated error gradient at the output layer.

Answer Writing Strategy for High Marks

RTU evaluators look for neat dataset tables, explicitly stated probability formulas, and clean graphical plots. Use a blue pen for text explanations and calculation lines. Use a black pen and ruler for drawing decision boundaries, neural network architectures, and clustering scatter plots.

In Part A, answer directly. If a question asks for the definition of a validation set, state clearly that it is a subset of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning hyperparameters.

In Part B, use clear computation grids. When executing a K-means step, draw a table showing the data points, their Euclidean distance to each centroid, and their assigned cluster before calculating the new centroid coordinates.

In Part C, precision in algorithms is critical. When solving a ten-mark Backpropagation problem, draw the complete network graph. Explicitly calculate the forward pass activations, calculate the error term for the output nodes, backpropagate the error to the hidden nodes, and write out the final updated weight matrices clearly.

Time Management During the Exam

Allocate exactly 20 minutes to Part A. Spend 40 minutes addressing the five short-answer questions in Part B. Reserve the remaining 120 minutes for the three long-answer questions in Part C. Computing covariance matrices for PCA, calculating multiple iterations of Apriori itemsets, and executing multi-layer weight updates requires steady focus and significant writing time to prevent arithmetic mistakes. This distribution guarantees you 40 minutes per major question, giving you time to double-check your fractional calculations. Use the final 10 minutes to verify your question numbering, ensure all matrix axes are labeled, and check that your gradient descent updates moved in the correct direction.