Question 1(a) [3 marks]

Define the following terms: (1) Artificial Intelligence (2) Expert System.

Answer:

Term	Definition
Artificial Intelligence	AI is a branch of computer science that creates machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
Expert System	An expert system is a computer program that uses knowledge and inference rules to solve problems that normally require human expertise in a specific domain.

AI characteristics: Learning, reasoning, perception
Expert system components: Knowledge base, inference engine

Mnemonic: "AI Learns, Expert Advises"

Question 1(b) [4 marks]

Compare Biological Neural Network and Artificial Neural Network.

Answer:

Aspect	Biological Neural Network	Artificial Neural Network
Processing	Parallel processing	Sequential/parallel processing
Speed	Slow (milliseconds)	Fast (nanoseconds)
Learning	Continuous learning	Batch/online learning
Storage	Distributed storage	Centralized storage

Biological: Complex, fault-tolerant, self-repairing
Artificial: Simple, precise, programmable

Mnemonic: "Bio is Complex, AI is Simple"

Question 1(c) [7 marks]

Explain types of AI with its applications.

Answer:

Type of AI	Description	Applications
Narrow AI	AI designed for specific tasks	Voice assistants, recommendation systems
General AI	AI with human-level intelligence	Not yet achieved
Super AI	AI exceeding human intelligence	Theoretical concept

graph TD
    A[Types of AI] --> B[Narrow AI]
    A --> C[General AI]
    A --> D[Super AI]
    B --> E[Siri, Alexa]
    B --> F[Netflix Recommendations]
    C --> G[Human-level Tasks]
    D --> H[Beyond Human Intelligence]

Current focus: Narrow AI dominates today's applications
Future goal: Achieving General AI safely

Mnemonic: "Narrow Now, General Goal, Super Scary"

Question 1(c) OR [7 marks]

Explain AI ethics and limitations.

Answer:

Ethics Aspect	Description
Privacy	Protecting personal data and user information
Bias	Ensuring fairness across different groups
Transparency	Making AI decisions explainable
Accountability	Determining responsibility for AI actions

Limitations:

Data dependency: Requires large, quality datasets
Computational power: Needs significant processing resources
Lack of creativity: Cannot truly create original concepts

Mnemonic: "Privacy, Bias, Transparency, Accountability"

Question 2(a) [3 marks]

Define the following terms: (1) Well posed Learning Problem (2) Machine Learning.

Answer:

Term	Definition
Well posed Learning Problem	A learning problem with clearly defined task (T), performance measure (P), and experience (E) where performance improves with experience.
Machine Learning	A subset of AI that enables computers to learn and improve automatically from experience without being explicitly programmed.

Well posed formula: T + P + E = Learning
ML advantage: Automatic improvement from data

Mnemonic: "Task, Performance, Experience"

Question 2(b) [4 marks]

Explain Reinforcement Learning along with terms used in it.

Answer:

Term	Description
Agent	The learner or decision maker
Environment	The world in which agent operates
Action	What agent can do in each state
State	Current situation of the agent
Reward	Feedback from environment

graph LR
    A[Agent] --> B[Action]
    B --> C[Environment]
    C --> D[State]
    C --> E[Reward]
    D --> A
    E --> A

Learning process: Trial and error approach
Goal: Maximize cumulative reward

Mnemonic: "Agent Acts, Environment States and Rewards"

Question 2(c) [7 marks]

Compare Supervised, Unsupervised and Reinforcement Learning.

Answer:

Aspect	Supervised	Unsupervised	Reinforcement
Data	Labeled data	Unlabeled data	Interactive data
Goal	Predict output	Find patterns	Maximize reward
Feedback	Immediate	None	Delayed
Examples	Classification	Clustering	Game playing

Supervised: Teacher-guided learning
Unsupervised: Self-discovery learning
Reinforcement: Trial-and-error learning

Mnemonic: "Supervised has Teacher, Unsupervised Discovers, Reinforcement Tries"

Question 2(a) OR [3 marks]

Write Key features of Reinforcement Learning.

Answer:

Feature	Description
Trial and Error	Learning through experimentation
Delayed Reward	Feedback comes after actions
Sequential Decision	Actions affect future states

No supervisor: Agent learns independently
Exploration vs Exploitation: Balance between trying new actions and using known good actions

Mnemonic: "Try, Delay, Sequence"

Question 2(b) OR [4 marks]

Explain Types of Reinforcement learning.

Answer:

Type	Description
Positive RL	Adding positive stimulus to increase behavior
Negative RL	Removing negative stimulus to increase behavior

Based on Learning:

Model-based: Agent learns environment model
Model-free: Agent learns directly from experience

Mnemonic: "Positive Adds, Negative Removes"

Question 2(c) OR [7 marks]

Explain approaches to implement Reinforcement Learning.

Answer:

Approach	Description	Example
Value-based	Learn value of states/actions	Q-Learning
Policy-based	Learn policy directly	Policy Gradient
Model-based	Learn environment model	Dynamic Programming

graph TD
    A[RL Approaches] --> B[Value-based]
    A --> C[Policy-based]
    A --> D[Model-based]
    B --> E[Q-Learning]
    C --> F[Policy Gradient]
    D --> G[Dynamic Programming]

Value-based: Estimates value functions
Policy-based: Optimizes policy parameters
Model-based: Uses environment model

Mnemonic: "Value, Policy, Model"

Question 3(a) [3 marks]

Describe the activation functions ReLU and sigmoid.

Answer:

Function	Formula	Range
ReLU	f(x) = max(0, x)	[0, ∞)
Sigmoid	f(x) = 1/(1 + e^(-x))	(0, 1)

ReLU advantage: No vanishing gradient problem
Sigmoid advantage: Smooth gradient, probabilistic output

Mnemonic: "ReLU Rectifies, Sigmoid Squashes"

Question 3(b) [4 marks]

Explain Multi-layer feed forward ANN.

Answer:

Component	Description
Input Layer	Receives input data
Hidden Layers	Process information (multiple layers)
Output Layer	Produces final result
Connections	Forward direction only

Information flow: Unidirectional from input to output
No cycles: No feedback connections

Mnemonic: "Input → Hidden → Output (Forward Only)"

Question 3(c) [7 marks]

Draw the structure of ANN and explain functionality of each of its components.

Answer:

graph LR
    A[Input Layer] --> B[Hidden Layer 1]
    B --> C[Hidden Layer 2]
    C --> D[Output Layer]
    
    subgraph "Components"
        E[Neurons]
        F[Weights]
        G[Bias]
        H[Activation Function]
    end

Component	Functionality
Neurons	Processing units that receive inputs and produce outputs
Weights	Connection strengths between neurons
Bias	Additional parameter to shift activation function
Activation Function	Introduces non-linearity to the network

Input layer: Receives and distributes input data
Hidden layers: Extract features and patterns
Output layer: Produces final classification or prediction
Connections: Weighted links between neurons

Mnemonic: "Neurons with Weights, Bias, and Activation"

Question 3(a) OR [3 marks]

Write a short note on Backpropagation.

Answer:

Aspect	Description
Purpose	Training algorithm for neural networks
Method	Gradient descent with chain rule
Direction	Backward error propagation

Process: Calculate error gradients backwards through network
Update: Adjust weights to minimize error

Mnemonic: "Back-ward Error Propagation"

Question 3(b) OR [4 marks]

Explain Single-layer feed forward network.

Answer:

Feature	Description
Structure	Input layer directly connected to output layer
Layers	Only input and output layers
Limitations	Can only solve linearly separable problems
Example	Perceptron

Capability: Limited to linear decision boundaries
Applications: Simple classification tasks

Mnemonic: "Single Layer, Linear Limits"

Question 3(c) OR [7 marks]

Draw and explain the architecture of Recurrent neural network.

Answer:

graph LR
    A[Input] --> B[Hidden State]
    B --> C[Output]
    B --> B[Self-loop]
    D[Previous State] --> B

Component	Function
Hidden State	Maintains memory of previous inputs
Recurrent Connection	Feedback from hidden state to itself
Sequence Processing	Handles sequential data

Memory: Retains information from previous time steps
Applications: Language modeling, speech recognition
Advantage: Can process variable-length sequences

Mnemonic: "Recurrent Remembers, Loops Back"

Question 4(a) [3 marks]

Define NLP and write down advantages of it.

Answer:

Term	Definition
NLP	Natural Language Processing - enables computers to understand, interpret, and generate human language

Advantages:

Human-computer interaction: Natural communication
Automation: Automated text processing and analysis
Accessibility: Voice interfaces for disabled users

Mnemonic: "Natural Language, Natural Interaction"

Question 4(b) [4 marks]

Compare NLU and NLG.

Answer:

Aspect	NLU (Understanding)	NLG (Generation)
Purpose	Interpret human language	Generate human language
Input	Text/Speech	Structured data
Output	Structured data	Text/Speech
Examples	Sentiment analysis	Text summarization

NLU: Converts unstructured text to structured data
NLG: Converts structured data to natural text

Mnemonic: "NLU Understands, NLG Generates"

Question 4(c) [7 marks]

Explain word tokenization and frequency distribution of words with suitable example.

Answer:

Process	Description	Example
Tokenization	Breaking text into individual words/tokens	"Hello world" → ["Hello", "world"]
Frequency Distribution	Counting occurrence of each token	{"Hello": 1, "world": 1}

Example:

Text: "The cat sat on the mat"
Tokens: ["The", "cat", "sat", "on", "the", "mat"]
Frequency: {"The": 1, "cat": 1, "sat": 1, "on": 1, "the": 1, "mat": 1}

Case sensitivity: "The" and "the" counted separately
Applications: Text analysis, search engines
Preprocessing: Essential step for NLP tasks

Mnemonic: "Tokenize then Count"

Question 4(a) OR [3 marks]

List disadvantages of NLP.

Answer:

Disadvantage	Description
Ambiguity	Multiple meanings of words/sentences
Context dependency	Meaning changes with context
Language complexity	Grammar rules and exceptions

Cultural variations: Different languages, dialects
Computational cost: Resource-intensive processing

Mnemonic: "Ambiguous, Contextual, Complex"

Question 4(b) OR [4 marks]

Explain types of ambiguities in NLP.

Answer:

Type	Description	Example
Lexical	Word has multiple meanings	"Bank" (financial/river)
Syntactic	Multiple parse trees possible	"I saw a man with a telescope"
Semantic	Multiple interpretations	"Flying planes can be dangerous"

Resolution: Context analysis, statistical models
Challenge: Major hurdle in NLP systems

Mnemonic: "Lexical words, Syntactic structure, Semantic meaning"

Question 4(c) OR [7 marks]

Explain stemming words and parts of speech(POS) tagging with suitable example.

Answer:

Process	Description	Example
Stemming	Reducing words to root/stem form	"running" → "run", "flies" → "fli"
POS Tagging	Assigning grammatical categories	"The/DT cat/NN runs/VB fast/RB"

Stemming Example:

Original: ["running", "runs", "runner"]
Stemmed: ["run", "run", "runner"]

POS Tagging Example:

Sentence: "The quick brown fox jumps"
Tagged: "The/DT quick/JJ brown/JJ fox/NN jumps/VB"

Stemming purpose: Reduce vocabulary size, group related words
POS purpose: Understand grammatical structure
Applications: Information retrieval, grammar checking

Mnemonic: "Stem to Root, Tag by Grammar"

Question 5(a) [3 marks]

Define the term word embedding and list various word embedding techniques.

Answer:

Term	Definition
Word Embedding	Dense vector representations of words that capture semantic relationships

Techniques:

TF-IDF: Term Frequency-Inverse Document Frequency
Bag of Words (BoW): Simple word occurrence counting
Word2Vec: Neural network-based embeddings

Mnemonic: "TF-IDF counts, BoW bags, Word2Vec vectorizes"

Question 5(b) [4 marks]

Explain about Challenges with TF-IDF and BoW.

Answer:

Method	Challenges
TF-IDF	Sparse vectors, no semantic similarity, high dimensionality
BoW	Order ignored, context lost, sparse representation

Common Issues:

Sparsity: Most vector elements are zero
No semantics: Similar words have different vectors
High dimensions: Memory and computation intensive

Mnemonic: "Sparse, No Semantics, High Dimensions"

Question 5(c) [7 marks]

Explain applications of NLP with suitable examples.

Answer:

Application	Description	Example
Machine Translation	Translate between languages	Google Translate
Sentiment Analysis	Determine emotional tone	Product review analysis
Question Answering	Answer questions from text	Chatbots, virtual assistants
Spam Detection	Identify unwanted emails	Email filters
Spelling Correction	Fix spelling errors	Auto-correct in text editors

graph TD
    A[NLP Applications] --> B[Machine Translation]
    A --> C[Sentiment Analysis]
    A --> D[Question Answering]
    A --> E[Spam Detection]
    A --> F[Spelling Correction]

Real-world impact: Improves human-computer interaction
Business value: Automates text processing tasks
Growing field: New applications emerging constantly

Mnemonic: "Translate, Sentiment, Question, Spam, Spell"

Question 5(a) OR [3 marks]

Describe the Glove(Global Vector for word representation).

Answer:

Aspect	Description
Purpose	Create word vectors using global corpus statistics
Method	Combines global matrix factorization and local context
Advantage	Captures both global and local statistical information

Global statistics: Uses word co-occurrence information
Pre-trained: Available trained vectors for common use

Mnemonic: "Global Vectors, Local Context"

Question 5(b) OR [4 marks]

Explain the Inverse Document Frequency (IDF).

Answer:

Component	Formula	Purpose
IDF	log(N/df)	Measure word importance across documents
N	Total documents	Corpus size
df	Document frequency	Documents containing the term

High IDF: Rare words (more informative)
Low IDF: Common words (less informative)
Application: Part of TF-IDF weighting scheme

Mnemonic: "Inverse Document, Rare is Important"

Question 5(c) OR [7 marks]

Explain calculation of TF(Term Frequency) for a document with suitable example.

Answer:

Method	Formula	Description
Raw TF	f(t,d)	Simple count of term in document
Normalized TF	f(t,d)/max(f(w,d))	Normalized by maximum frequency
Log TF	1 + log(f(t,d))	Logarithmic scaling

Example Document: "The cat sat on the mat. The mat was soft."

Term	Count	Raw TF	Normalized TF	Log TF
"the"	3	3	1.0	1.48
"cat"	1	1	0.33	1.0
"mat"	2	2	0.67	1.30

Calculation Steps:

Count each term occurrence
Apply chosen TF formula
Use in TF-IDF calculation

Mnemonic: "Count, Normalize, Log"