(SEM V) THEORY EXAMINATION 2022-23 MACHINE LEARNING TECHNIQUES

SECTION A – Short Answer Type Questions (2 Marks each)

(a) Discuss Model Representation of Artificial Neuron.

An artificial neuron is a mathematical model inspired by a biological neuron. It processes input signals to produce an output based on weights and activation function.

Mathematical Model:

y=f(∑i=1nwixi+b)y = f\left(\sum_{i=1}^{n} w_i x_i + b\right)y=f(i=1∑n​wi​xi​+b)

where:

xix_ixi​: Input signals

wiw_iwi​: Weights

bbb: Bias

fff: Activation function (e.g., sigmoid, ReLU)

yyy: Output

Explanation:

Inputs are multiplied by weights and summed with bias.

The activation function introduces non-linearity.

The neuron "fires" (produces output) if the total input exceeds a threshold.

Example: Used in perceptrons and neural networks for pattern recognition.

(b) Differentiate Between Gradient Descent and Stochastic Gradient Descent.

Conclusion:
SGD is preferred for large datasets and online learning, while GD suits small or static datasets.

 SECTION B – Long Answer Type Questions (10 Marks each)

(a) Explain Supervised and Unsupervised Learning Techniques.

1. Supervised Learning:

Input data is labeled, meaning the correct output is known.

The model learns a mapping function f(x)→yf(x) → yf(x)→y.

Examples:

Regression: Predicting house prices.                      Classification: Spam email detection.

Algorithms: Linear Regression, Decision Trees, SVM, Neural Networks.

2. Unsupervised Learning:

No labeled outputs; the model finds hidden patterns in data.

Examples:

Clustering: Customer segmentation using K-Means.

Association: Market basket analysis using Apriori algorithm.

Key Difference:
Supervised learning focuses on prediction, while unsupervised focuses on pattern discovery.

(b) Explain Various Types of Activation Functions with Examples.

Activation functions introduce non-linearity to neural networks, enabling complex pattern learning.

1. Step Function:

f(x)={1if x≥00if x<0f(x) = \begin{cases} 1 & \text{if } x ≥ 0 \\ 0 & \text{if } x < 0 \end{cases}f(x)={10​if x≥0if x<0​

Used in perceptrons.

2. Sigmoid Function:

f(x)=11+e−xf(x) = \frac{1}{1 + e^{-x}}f(x)=1+e−x1​

Smooth curve; output between 0 and 1. Used in binary classification.

3. Tanh Function:

f(x)=tanh⁡(x)f(x) = \tanh(x)f(x)=tanh(x)

Range = (-1, 1); centered around zero.

4. ReLU (Rectified Linear Unit):

f(x)=max⁡(0,x)f(x) = \max(0, x)f(x)=max(0,x)

Efficient for deep networks; reduces vanishing gradient problem.

5. Leaky ReLU:
Allows small negative slope for negative inputs to avoid dead neurons.

SECTION C – Very Long Answer Type Questions (10 Marks each)

(a) Demonstrate K-Nearest Neighbors (KNN) Algorithm for Classification with Example.

Definition:
KNN is an instance-based learning algorithm that classifies a new sample based on the majority label of its K nearest neighbors.

Algorithm Steps:

Choose the number of neighbors KKK.

Compute distance between test sample and all training samples (e.g., Euclidean distance).

Select K nearest samples.

Assign the most frequent class label among them to the test point.

Example:
If K=3 and among the nearest points two belong to class “A” and one to “B,” the new point is classified as 
Class A.

Applications:

Image recognition.

Recommendation systems.

(b) Explain Different Layers Used in Convolutional Neural Network (CNN) with Suitable Examples.

1. Input Layer:
Accepts image data (e.g., 28×28×3 for RGB).

2. Convolutional Layer:
Applies filters to extract features like edges or textures.

Y=X∗W+bY = X * W + bY=X∗W+b

Example: Edge detection using 3×3 filter.

3. Activation Layer (ReLU):
Applies non-linearity to feature maps.

4. Pooling Layer:
Reduces spatial dimensions using Max or Average Pooling, retaining important information.

5. Fully Connected Layer:
Combines all extracted features for classification.

6. Output Layer:
Applies softmax activation for multi-class classification (e.g., handwritten digit recognition).