(SEM VII) THEORY EXAMINATION 2024-25 MACHINE LEARNING

SECTION A

(2 × 10 = 20 marks | Short Answers)

a) AI, ML and Deep Learning

Artificial Intelligence (AI): Broad field where machines perform tasks that require human intelligence.

Machine Learning (ML): Subset of AI where machines learn patterns from data.

Deep Learning: Subset of ML using multi-layer neural networks to learn complex patterns.

b) Types of Machine Learning

Supervised Learning: Uses labeled data (e.g., house price prediction).

Unsupervised Learning: Finds patterns in unlabeled data (e.g., clustering customers).

Reinforcement Learning: Learns by reward and penalty (e.g., game playing).

c) Single learning rate for all features

Using one learning rate may cause slow convergence for some features and overshooting for others, especially when features have different scales.

d) Training vs Testing data

Training data: Used to train the model.

Testing data: Used to evaluate model performance on unseen data.

e) Dimensionality reduction

It reduces the number of input features while retaining important information, improving efficiency and reducing overfitting (e.g., PCA).

f) Hierarchical clustering

A clustering method that builds a hierarchy of clusters using agglomerative or divisive approaches, represented by a dendrogram.

g) Loss function

A loss function measures the difference between predicted output and actual output, guiding model optimization (e.g., MSE, cross-entropy).

h) Overfitting

Overfitting occurs when a model performs well on training data but poorly on new data due to excessive complexity.

i) Genetic algorithm for scheduling

Steps:                                                         Initialize population

Evaluate fitness                                          Selection

Crossover                                                   Mutation

Termination                                                Used to find optimal schedules efficiently.

j) Kernel in Gaussian processes

A kernel defines the similarity between data points, controlling smoothness and structure of functions in Gaussian processes.

SECTION B

(Attempt any 3 | 10 marks each)

a) Artificial Intelligence and its industrial impact

AI improves automation, decision-making, quality control, predictive maintenance, robotics, and supply chain optimization across industries.

b) Support Vector Machines (SVM)

SVM finds an optimal hyperplane that maximizes the margin between classes. Kernels allow it to handle non-linear data.

c) Expectation-Maximization (EM) algorithm

EM is an iterative algorithm used to estimate parameters in probabilistic models with latent variables through:

Expectation step                                               Maximization step

d) Challenges of Backpropagation

Vanishing gradients                                          Slow convergence

Local minima                                                    High computational cost

e) Hidden Markov Models (HMM)

HMMs are probabilistic models for sequential data.
Applications: speech recognition, bioinformatics, time-series prediction.

SECTION C

Q3 (Attempt any one)

a) Steps in designing a learning system

Problem definition                                            Data collection

Feature selection                                               Model selection

Training                                                              Evaluation

Deployment

b) ML applications in mechanical engineering

Predictive maintenance                                     Manufacturing defect detection

Energy optimization                                          Robotics and automation

Q4 (Attempt any one)

a) Bayesian Decision Theory

It provides a probabilistic framework for classification by minimizing expected risk using prior probabilities and likelihoods.

b) Bias and variance

Bias: Error due to overly simple model               Variance: Error due to overly complex model
Trade-off affects generalization.

Q5 (Attempt any one)

a) Principal Component Analysis (PCA)

PCA transforms correlated features into uncorrelated components, reducing dimensionality while preserving variance.

b) Clustering vs Classification                           Clustering: Unsupervised grouping

Classification: Supervised labeling

Q6 (Attempt any one)

a) Receptive field in CNN

The receptive field is the region of input influencing a neuron. It depends on kernel size, stride, and number of layers.

b) Kernel function in SVM

Kernel functions map data into higher-dimensional space for separation.
Examples:                                                               Linear kernel – text classification

Polynomial kernel – image recognition                 RBF kernel – non-linear problems

Q7 (Attempt any one)

a) Bayesian estimation vs MLE

MLE: Uses data only, simple but overfits

Bayesian: Uses prior knowledge, more robust but computationally heavy

b) Reinforcement learning vs Deep learning

Reinforcement Learning: Learns via rewards      Deep Learning: Learns hierarchical features
RL focuses on decision-making, DL on representation learning.