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🎯 1CS101 Introduction to AI & ML β€” Ultimate Exam Master Bank

Syllabus-aligned Β· Every concept mapped Β· June 2023 to April 2025 Β· Theory, Numericals, Algorithms & Python

πŸ“Œ How to use this resource: Each question has two buttons β€” πŸ“˜ Course opens the imp_pdf/ai_ml/1CS101 full course.pdf at the exact concept page, πŸ“ Exam opens the imp_pdf/ai_ml/1CS101 question bank.pdf at the relevant past exam question.
πŸ”₯ High Yield = appeared in 3+ exam papers. All questions are unique & non-repeating across units.
Unit I: Foundational Concepts in Artificial Intelligence (5 hrs)
1.1 Introduction to Computational Systems
With a neat diagram, explain the basic structure of a computer system (Input Unit, CPU, Output Unit, Memory). Diagram Apr 25 Mar 24 πŸ“˜ Course Pg 376 πŸ“ Exam Pg 22
Differentiate between Primary Memory (RAM/ROM) and Secondary Memory. Explain the role of Cache and Registers. Theory Dec 23 πŸ“˜ Course Pg 386 πŸ“ Exam Pg 10
Describe the functions of Control Unit (CU) and Arithmetic Logic Unit (ALU) within the CPU. Theory Dec 23 πŸ“˜ Course Pg 381 πŸ“ Exam Pg 10
1.2 Problem Formulation & Problem Solving
Explain the problem formulation steps: Define problem, scope, objectives, data requirements, evaluation methods, constraints. Theory Apr 25 Feb 24 πŸ“˜ Course Pg 390 πŸ“ Exam Pg 12
Outline the general problem-solving process: Analyzing, Algorithm development, Coding, Testing & Debugging, Maintenance. High Yield Feb 25 June 23 πŸ“˜ Course Pg 392 πŸ“ Exam Pg 14
1.3 Intelligence vs Artificial Intelligence
Define Artificial Intelligence. Compare and contrast Human Intelligence and Artificial Intelligence across dimensions: Adapting, Digital vs Analogue, Thinking & Reasoning. High Yield Diagram Apr 25 Dec 23 June 23 πŸ“˜ Course Pg 338 πŸ“ Exam Pg 17
1.4 History of AI
Trace the history of AI across four phases: Foundations (1941–1950), Birth & Early Excitement (1956–1970s), Expert Systems (1980–2000), Modern AI (2000–Present). Theory Feb 25 Mar 24 πŸ“˜ Course Pg 341 πŸ“ Exam Pg 22
Explain the Turing Test (1950) and its significance in AI. Describe ELIZA (1966), Deep Blue (1997), and IBM Watson (2011). High Yield Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 344 πŸ“ Exam Pg 6
1.5 Data vs Information vs Knowledge
Distinguish between Data, Information, and Knowledge with real-world examples. Explain the transformation pipeline. High Yield Feb 25 June 23 πŸ“˜ Course Pg 355 πŸ“ Exam Pg 1
1.6 Knowledge Representation
Explain Rule-based Knowledge Representation (IF-THEN rules) with examples like MYCIN. Theory Mar 24 πŸ“˜ Course Pg 361 πŸ“ Exam Pg 1
Describe Structural Knowledge Representation and explain why it is more powerful than rule-based systems for reasoning. Theory Feb 24 πŸ“˜ Course Pg 362 πŸ“ Exam Pg 1
1.7 Jargons of AI
Define and exemplify AI jargons: Algorithm, Machine Learning, Neural Network, Training Data, Natural Language Processing, Prediction. High Yield Apr 25 Dec 23 πŸ“˜ Course Pg 358 πŸ“ Exam Pg 5
1.8 Importance & Applications of AI
Discuss the importance and applications of AI in different domains: Healthcare, Finance, Autonomous Vehicles, Education, Entertainment. Theory Feb 25 June 23 πŸ“˜ Course Pg 363 πŸ“ Exam Pg 22
Explain any five Computer Vision applications (Face Recognition, Autonomous Vehicles, Object Detection, Semantic Segmentation, Emotion Recognition). Diagram Mar 24 πŸ“˜ Course Pg 363 πŸ“ Exam Pg 1
Describe any five Natural Language Processing applications (Machine Translation, Sentiment Analysis, Chatbots, NER, Speech Recognition). Theory Feb 24 πŸ“˜ Course Pg 369 πŸ“ Exam Pg 1
Unit II: Data Exploration (6 hrs)
2.1 Types of Data
Classify and explain Structured, Semi-structured, and Unstructured Data with examples (SQL tables, JSON, images/video). High Yield Apr 25 Feb 24 πŸ“˜ Course Pg 398 πŸ“ Exam Pg 2
2.2 Data Collection Methods
Explain any six data collection methods: Interviews, Questionnaires & Surveys, Observations, Documents & Records, Focus Groups, Oral Histories, Sensors, Kaggle, WHO/World Bank portals, Open Government Portals. Theory Mar 24 πŸ“˜ Course Pg 409 πŸ“ Exam Pg 1
2.3 Data Characteristics & Issues
What are the different types of data issues that can arise during collection? Explain Erroneous Data, Invalid/Null values, Missing Data, and Outliers. Theory Feb 25 Feb 24 πŸ“˜ Course Pg 435 πŸ“ Exam Pg 12
2.4 Handling Missing Values
Discuss six methods for handling missing values: Ignore the tuple, Delete column, Fill manually, Global constant, Attribute mean/mode, Class-specific mean/mode. Top Repeat Apr 25 Feb 25 Dec 23 June 23 πŸ“˜ Course Pg 419 πŸ“ Exam Pg 1
Numerical: On the Titanic dataset, identify missing values in Age and Cabin. Fill Age using mean of survived class, Cabin using mode of survived class. Numerical Feb 25 πŸ“˜ Course Pg 430 πŸ“ Exam Pg 1
Explain the impact of improperly handled missing data on machine learning model performance. Theory Apr 25 June 23 πŸ“˜ Course Pg 421 πŸ“ Exam Pg 1
2.5 Data Visualization
What is a Box Plot? Explain the five-number summary (Min, Q1, Median, Q3, Max) and how to identify outliers using IQR. High Yield Diagram Numerical Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 449 πŸ“ Exam Pg 4
Numerical: Calculate Q1, Q3, IQR and identify outliers for dataset: 52, 55, 71, 75, 81, 83, 85, 89, 90, 90, 99, 100, 100. Numerical Feb 25 Mar 24 πŸ“˜ Course Pg 453 πŸ“ Exam Pg 1
Explain Scatter Plots and Bubble Charts. How can a bubble chart visualize 4 features simultaneously? Diagram Mar 24 πŸ“˜ Course Pg 440 πŸ“ Exam Pg 4
Differentiate between Bar Chart and Histogram. When should each be used? Theory Feb 24 πŸ“˜ Course Pg 474 πŸ“ Exam Pg 4
What is a Line Chart? Explain its use in visualizing trends over time with an example. Diagram June 23 πŸ“˜ Course Pg 478 πŸ“ Exam Pg 4
Explain Exploratory Data Analysis (EDA) using the Titanic dataset as an example. What insights can be drawn? Theory Mar 24 πŸ“˜ Course Pg 481 πŸ“ Exam Pg 6
Unit III: Introduction to State Space & State Space Search (5 hrs)
3.1 State, State Space, State Space Search
Define State, State Space, and State Space Search with examples: 8-Puzzle, Tic-Tac-Toe, Vacuum Cleaner Problem. Top Repeat Diagram Apr 25 Feb 25 Dec 23 June 23 πŸ“˜ Course Pg 486 πŸ“ Exam Pg 10
Formally represent a search problem as P = {S, A, Action(S), Result(S,a), Cost(S,a)} with the 8-puzzle as an example. Theory Mar 24 πŸ“˜ Course Pg 499 πŸ“ Exam Pg 12
Explain Problem Reduction with AND-OR graphs. Give an example (Build a House, Unlock Door, Matrix Multiplication). Diagram Feb 24 πŸ“˜ Course Pg 522 πŸ“ Exam Pg 14
3.2 Hill Climbing & Steepest Ascent Hill Climbing
Write the Simple Hill Climbing algorithm. Solve 8-puzzle using heuristic h(n) = number of tiles in correct place. High Yield Algorithm Numerical Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 569 πŸ“ Exam Pg 2
Write the Steepest-Ascent Hill Climbing algorithm. Solve 8-puzzle using Manhattan distance heuristic. Show all steps. High Yield Algorithm Numerical Feb 25 Dec 23 πŸ“˜ Course Pg 582 πŸ“ Exam Pg 4
Explain the Generate-and-Test algorithm with an example (password cracking). Compare with Hill Climbing. Algorithm Mar 24 πŸ“˜ Course Pg 535 πŸ“ Exam Pg 4
Describe the limitations of Hill Climbing: Local Maxima, Plateaus, and Ridges. What solutions exist (Random Restart, Simulated Annealing)? High Yield Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 589 πŸ“ Exam Pg 6
3.3 Solving Problems using State Space Search
8-Puzzle Heuristic Calculation: For a given initial state, compute h-values using (a) Manhattan distance, (b) Number of misplaced tiles, (c) Number of tiles in correct place. High Yield Numerical Apr 25 Feb 25 Dec 23 πŸ“˜ Course Pg 578 πŸ“ Exam Pg 4
TSP – Brute Force: For a 4-city/6-city TSP, find the best tour and tour length by generating all permutations. High Yield Numerical Feb 25 Mar 24 June 23 πŸ“˜ Course Pg 543 πŸ“ Exam Pg 6
TSP – Nearest Neighbour Heuristic: Apply NNH on a 6-city symmetric TSP. Compare result with brute-force optimal. Numerical Apr 25 Dec 23 πŸ“˜ Course Pg 555 πŸ“ Exam Pg 3
Compare BFS and DFS for state space search. Write algorithms for both and discuss advantages/disadvantages. Algorithm Feb 24 πŸ“˜ Course Pg 508 πŸ“ Exam Pg 14
Unit IV: Introduction to Machine Learning (10 hrs)
4.1 Role of ML in AI & ML Life Cycle
Define Machine Learning. Explain the ML Life Cycle with a diagram: Gathering Data β†’ Preparation β†’ Wrangling β†’ Analysis β†’ Train β†’ Test β†’ Deployment. Diagram Apr 25 Feb 24 πŸ“˜ Course Pg 603 πŸ“ Exam Pg 6
Differentiate between AI, ML, and DL. Explain how ML is a subset of AI and DL is a subset of ML. High Yield Diagram Apr 25 Dec 23 June 23 πŸ“˜ Course Pg 328 πŸ“ Exam Pg 17
4.2 Applications & Jargons of ML
Describe the role of Machine Learning in different domains: Healthcare, Finance, Retail, Autonomous Vehicles, Entertainment. Theory Feb 25 Mar 24 πŸ“˜ Course Pg 602 πŸ“ Exam Pg 6
Explain Overfitting and Underfitting with regression examples. What are the causes and solutions for each? High Yield Diagram Feb 25 Mar 24 June 23 πŸ“˜ Course Pg 612 πŸ“ Exam Pg 17
4.3 Supervised Learning – Classification vs Regression
Differentiate between Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning with suitable examples. Top Repeat Apr 25 Feb 25 Dec 23 June 23 πŸ“˜ Course Pg 621 πŸ“ Exam Pg 4
Distinguish between Classification and Regression problems. List three algorithms for each with real-world examples. High Yield Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 624 πŸ“ Exam Pg 6
4.4 KNN for Classification & Regression
Write the KNN Classification algorithm. How to choose the value of k? What is the significance of odd k? High Yield Algorithm Apr 25 Dec 23 πŸ“˜ Course Pg 634 πŸ“ Exam Pg 9
Numerical – KNN Classification: Classify a new patient (BP=135, Cholesterol=215) as Healthy/Diseased using distance-weighted 3-NN (Euclidean distance). Top Repeat Numerical Apr 25 Feb 25 June 23 πŸ“˜ Course Pg 653 πŸ“ Exam Pg 18
Numerical – KNN Classification: Predict species of an egg (weight=11g, volume=9cmΒ³) using simple 3-NN and distance-weighted 3-NN. Numerical Feb 24 June 23 πŸ“˜ Course Pg 639 πŸ“ Exam Pg 15
Numerical – KNN Regression: Predict house price (size=1600 sq m) using KNN regression with K=3 after Min-Max normalization. High Yield Numerical Feb 25 June 23 πŸ“˜ Course Pg 669 πŸ“ Exam Pg 2
Numerical – Weighted KNN Regression: Predict fish caught by 15 fishermen in 2 hrs using uniform-weighted 3-NN after normalization. Numerical Feb 24 πŸ“˜ Course Pg 680 πŸ“ Exam Pg 23
Explain performance metrics for classification: Confusion Matrix, Accuracy, Precision, Recall, F1-Score. Theory June 23 πŸ“˜ Course Pg 662 πŸ“ Exam Pg 7
Explain loss functions for regression: MAE, MSE, RMSE, MAPE with formulas and interpretation. Theory Mar 24 πŸ“˜ Course Pg 686 πŸ“ Exam Pg 16
4.5 Unsupervised Learning – K-Means Algorithm
Write the K-Means Clustering algorithm. Discuss its applications in customer segmentation, image compression, anomaly detection. High Yield Algorithm Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 692 πŸ“ Exam Pg 2
Numerical – K-Means: Cluster 8 points A1(2,10)...A8(4,9) into 3 clusters with initial centroids A1, B1, C1. Show 2 iterations with centroid updates. Top Repeat Numerical Apr 25 Feb 25 Dec 23 June 23 πŸ“˜ Course Pg 693 πŸ“ Exam Pg 8
Numerical – K-Means: Cluster 7 points (1,1)...(7,12) into K=2 clusters. Show cluster membership and centroids after 2 iterations. Numerical Feb 24 Dec 23 πŸ“˜ Course Pg 707 πŸ“ Exam Pg 9
4.6 Biological Neural Networks to Artificial Neural Networks
Explain the analogy from Biological Neuron to Artificial Neuron. Draw a labeled diagram of a perceptron. Diagram Feb 25 Mar 24 πŸ“˜ Course Pg 323 πŸ“ Exam Pg 8
Explain Hebbian Learning ("Neurons that fire together, wire together") with an example. Theory June 23 πŸ“˜ Course Pg 343 πŸ“ Exam Pg 14
4.7 Perceptron Learning
Explain the Perceptron Learning Rule. Train a perceptron for AND gate. Show computation for one epoch (initial weights=1, bias=1, Ξ·=0.2). Top Repeat Numerical Apr 25 Mar 24 Dec 23 June 23 πŸ“˜ Course Pg 324 πŸ“ Exam Pg 3
Numerical – 1D Perceptron: Train perceptron on 1D patterns (0.0, 0.18, 0.43 β†’ class 0; 0.61, 0.77, 0.93, 1.0 β†’ class 1) with Ξ·=0.12 for two epochs. Numerical Mar 24 Feb 24 πŸ“˜ Course Pg 324 πŸ“ Exam Pg 8
Numerical – 2D Perceptron: Train perceptron on 7 points (x1={1..7}, x2={1,3,5,3,2,6,1}) with labels {1,0,1,0,0,1,1} for one epoch. Numerical Apr 25 June 23 πŸ“˜ Course Pg 324 πŸ“ Exam Pg 18
Why can't a single perceptron solve XOR? Show how a multi-layer network can solve XOR using 2 hidden neurons. Rare Diagram Mar 24 πŸ“˜ Course Pg 326 πŸ“ Exam Pg 14
4.8 Reinforcement Learning – Q-Learning
Define Reinforcement Learning. Explain the Q-Learning algorithm with a 5-room building example (states 0-5, goal=room 5). Top Repeat Numerical Apr 25 Feb 25 Dec 23 June 23 πŸ“˜ Course Pg 713 πŸ“ Exam Pg 2
Write the Q-value update equation: Q(s,a) = R(s,a) + Ξ³ Β· max Q(s', a'). Explain the role of R-matrix, Q-matrix, and Ξ³ (gamma). High Yield Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 715 πŸ“ Exam Pg 17
Numerical – Q-Learning: Trace two episodes of Q-learning (Ξ³=0.8) starting from state 1, then state 3. Show updated Q-matrix after each episode. Numerical Feb 25 Dec 23 πŸ“˜ Course Pg 716 πŸ“ Exam Pg 10
Explain Grid-based RL: 3Γ—3 grid with Start, Goal (+5), Danger (βˆ’10). Agent actions: Left, Right, Up, Down. Rare Dec 23 πŸ“˜ Course Pg 713 πŸ“ Exam Pg 11
Unit V: Introduction to Deep Learning (4 hrs)
5.1 Role of DL in AI & ML vs DL
Explain the role of Deep Learning in AI. Differentiate Machine Learning vs Deep Learning with a comparison table. High Yield Apr 25 Feb 24 June 23 πŸ“˜ Course Pg 719 πŸ“ Exam Pg 12
5.2 Types of Deep Networks
List and describe the four major architectures of Deep Networks: UPNs (Autoencoders, DBNs, GANs), CNNs, RNNs, Recursive Neural Networks. State 2 example networks and use cases for each. Top Repeat Feb 25 Mar 24 Dec 23 June 23 πŸ“˜ Course Pg 723 πŸ“ Exam Pg 2
Explain Convolutional Neural Networks (CNNs) with a diagram showing convolution, pooling, and fully connected layers. List use cases. Diagram Apr 25 June 23 πŸ“˜ Course Pg 732 πŸ“ Exam Pg 12
Explain Recurrent Neural Networks (RNNs) and their use in sequence data. Differentiate RNNs from Recursive Neural Networks. Theory Feb 24 πŸ“˜ Course Pg 737 πŸ“ Exam Pg 16
Describe Autoencoders (Encoder-Decoder) and Generative Adversarial Networks (GANs) with applications. Theory Dec 23 πŸ“˜ Course Pg 724 πŸ“ Exam Pg 11
Discuss Symmetric and Asymmetric Neural Network architectures with proper diagrams. Rare Diagram Feb 24 πŸ“˜ Course Pg 723 πŸ“ Exam Pg 16
6. Python Programming & Practical Implementation for ML
6.1 Data Handling with Pandas & NumPy
Write a Python program using Pandas to read a CSV file, display first/last 5 rows using head()/tail(), and get data summary using describe(). Python Apr 25 πŸ“˜ Course Pg 170 πŸ“ Exam Pg 8
Write a Python program to handle missing values in four different ways: dropna(), fillna with mean, fillna with mode, fillna with constant. Python Feb 24 June 23 πŸ“˜ Course Pg 170 πŸ“ Exam Pg 24
Create a Pandas DataFrame from a dictionary. Perform operations: loc[], iloc[], add/rename/delete columns, Boolean indexing. Python Feb 25 πŸ“˜ Course Pg 191 πŸ“ Exam Pg 8
Write Python code using Matplotlib to plot: (a) Line chart with markers, (b) Scatter plot with color-coded classes, (c) Histogram with bins, (d) Bar chart. Python Diagram Mar 24 πŸ“˜ Course Pg 229 πŸ“ Exam Pg 8
Implement NumPy operations: transpose(), flatten(), concatenate() with axis=0 and axis=1, broadcasting, zeros(), ones(), arange() with reshape(). Python Feb 24 πŸ“˜ Course Pg 121 πŸ“ Exam Pg 8
6.2 ML Model Implementation in Python
Write a program to read Iris/Boston data, split into train/test sets based on user input (without sklearn), and print shapes. Python Apr 25 Mar 24 πŸ“˜ Course Pg 306 πŸ“ Exam Pg 8
Implement KNN Classification on Iris dataset using sklearn. Use distance-weighted KNN (K=user input) and print accuracy, classification report. Python Apr 25 πŸ“˜ Course Pg 310 πŸ“ Exam Pg 16
Implement Weighted KNN Regression from scratch (without sklearn) to predict Boston house prices. Calculate MAE, MSE, RMSE, MAPE. Python Feb 25 πŸ“˜ Course Pg 316 πŸ“ Exam Pg 16
Implement K-Means Clustering on Iris dataset. Print centroids and cluster assignments for test data. Show both sklearn and user-defined versions. Python Feb 25 πŸ“˜ Course Pg 319 πŸ“ Exam Pg 16
Code a Perceptron from scratch for AND gate with weight update loop. Plot the decision boundary using matplotlib. Python Mar 24 πŸ“˜ Course Pg 324 πŸ“ Exam Pg 16
Implement TSP Brute-Force and Nearest Neighbour Heuristic for 6 cities. Print best tour, tour length, and execution time. Python Apr 25 πŸ“˜ Course Pg 299 πŸ“ Exam Pg 16
Write a program to calculate descriptive statistics (mean, variance, standard deviation, median, mode, range) manually without libraries. Python Mar 24 πŸ“˜ Course Pg 287 πŸ“ Exam Pg 8

πŸ“‹ Exam Pattern Summary: All questions compulsory. Section-wise separate answer books. Q1-Q6 with sub-parts (A, B, C). Questions blend theory (BL1-BL2), numerical/application (BL3-BL4), and analysis/design (BL5-BL6).
πŸ”₯ Highest Priority: 8-Puzzle Hill Climbing, KNN (Classification & Regression), K-Means Clustering, Perceptron AND gate, Q-Learning, Missing Value Handling, DNN Architectures, Python ML programs.
πŸ“˜ Course = imp_pdf/ai_ml/1CS101 full course.pdf (concept slides)  |  πŸ“ Exam = imp_pdf/ai_ml/1CS101 question bank.pdf (past papers)