Exam Gist
All Important topic, related to exam
ML
Exam will be on paper only, even for MCQ ans
Basic Q/N for exam
Duration: 3 hours
Mode: Question paper will on LMS and upload their only
Exam Type: Closed book
Coding: Not required
Derivations: Only one solved derivation may be asked
Focus: Concept-driven problems
Answer Format: No lengthy answers required
Device: Paper only (no iPad)
Answer Submission: Write all answers (MCQ and subjective) on paper, not on LMS
Calculator: Scientific calculators allow
Topics
ML Definitions
Data Pre-processing
Performance Measures
Training and Testing
Cross Validation
Correlation
Imputation
Redundant Data Removal
Linear Regression
Regression Terminology
Gradient Descent & Types
Ridge & Lasso Regression
Normal Equation
Regularization
Bias-Variance Tradeoff
Locally Weighted Regression (LWR)
Derivation
Classification
Logistic Regression
Multiclass Classifier
Cost Function
Accuracy, Precision, Recall, TP, TN
ROC Curve (Evaluation Matrix)
Confusion Matrix
Bayesian Classifier
Bayes Theorem
Classifier Terminology
Naive Bayes Theorem
Covariance Matrix
Univariate / Multivariate Densities
Risk Function
Decision Tree
Structure & Terminology (Root, Leaf, etc.)
Tree Design
Impurity Measures: Gini, Entropy
CART, ID3 (Multiclass)
Information Gain
Decision Tree Regularization
Random Forest
Ensemble Methods
Voting Classifier
Bagging
Random Subspace Method
OOB Instances
Boosting
Regression-based Random Forest
SVM (Support Vector Machine)
Terminology
Regularization
Parameters & Hyperparameters
Cost Function
Non-linear SVM
Kernel Techniques
Derivation of Optimal Margin
K-Means
Unsupervised Learning
Applications
Algorithm (with Pseudocode)
Cost Function
Elbow Method / Silhouette Score
PCA (Principal Component Analysis)
Eigenvectors & Eigenvalues
Eigen Decomposition
SVD & Types of SVD
Variance & Covariance
U, Sigma Matrix
Projection & Manifold Learning
Eigenface Problem
Neural Networks
Importance
Perceptron, LTU, TLU
Weights & Bias
Notation & Matrix Formulation
Forward Pass
Activation Functions
Gradient Descent
Backpropagation
Regularization
CNN (Convolutional Neural Network)
Architecture
Purpose
Kernels, Striding, Padding
Pooling
Example Architectures
Backpropagation in CNN
Autoencoders
Architecture
Loss Function
Variational Autoencoders (VAE)
Generative Models
RNN (Recurrent Neural Network)
Structure
Numerical problems
Information Gain
Design a Spam Filter using Bayesian Classification
Neural Network Backpropagation
Confusion Matrices
Boosting
Equation of a Regression Line (given data points)
Distance from Hyperplane – SVM
Logistic Regression
Data Preprocessing Techniques
CNN Concepts – Striding, etc.
DSA
Paper will be MCQ only
Topics
After Minor (weightage 70%)
Red-Black Trees
Red-Black Tree Deletion
Augmenting Data Structures
Disjoint-Set
Disjoint-Set as Trees
Graph Algorithms
Breadth-First Search (BFS)
Dijkstra’s Algorithm
Flow Networks
Ford-Fulkerson Method
Bipartite Maximum Matching
Dynamic Programming
Longest Common Subsequence (LCS)
Complexity Classes
P, NP, and NP-Completeness
Before Minor (weightage 30%)
About Algo
Insertion Sort
Merge Sort
Recurrence Relation
Hashing
Binary Search Tree
Binary Search Tree Deletion
ODS
Topics
Fact-1 NLLP (Nonlinear Linear Programming)
Theory
Graphical Method
Vector and Tuple, Vector Space
Convex Set, Convex Combination
Convex Function (∪ shaped), Concave Function (∩ shaped)
Hessian Matrix
Determinant calculations
Symmetric Matrix
Positive/Negative Definite Hessian (Local Minima/Maxima)
Convexity/Concavity via Hessian
Global Minima/Maxima
Orthogonal Vectors
Method
First & Second Order Conditions
Line Search Method
Unimodal Function
Golden Section Method
Fibonacci Search Method
Decent method
Taylor Series Expansion
Newton's Method
Single Variable Newton Method
Multivariable Extension
Steepest Descent Method
Directional Derivatives
Quasi Newton Method
Conjugate Gradient Method
Conjugate Directions
Least Square Approximations
Fact-2
Graphical Method
Simplex & Big M Method
Lagrange Multipliers & KKT Conditions
Penalty and Barrier Methods
Frank-Wolfe Algorithm
Patern
Fact-1
4 Subjective Questions (guess)
Remaining Objective Questions
Fact-2
4 Subjective Questions
Remaining Objective Questions
AI
Topics
Basic AI
AI Agent
Solving Problems by Searching
Knowledge-Based Agents
First Order Logic
Local Search
Constraint Satisfaction Problems
Adversarial Search and Games
Markov Decision Process and Reinforcement Learning
Bayesian Networks
Automated Planning
Patern
Question Type:
3 Subjective Questions
Remaining Objective Questions
Difficulty Level:
25% Easy
50% Medium
25% Difficult
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