# Exam Gist

## ML

<mark style="color:red;">**Exam will be on paper only, even for MCQ ans**</mark>

<details>

<summary>Basic Q/N for exam</summary>

* **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

</details>

<details>

<summary>Topics</summary>

1. **ML Definitions**
2. **Data Pre-processing**
   * Performance Measures
   * Training and Testing
   * Cross Validation
   * Correlation
   * Imputation
   * Redundant Data Removal
3. **Linear Regression**
   * Regression Terminology
   * Gradient Descent & Types
   * Ridge & Lasso Regression
   * Normal Equation
   * Regularization
   * Bias-Variance Tradeoff
   * Locally Weighted Regression (LWR)
   * Derivation
4. **Classification**
   * Logistic Regression
   * Multiclass Classifier
   * Cost Function
   * Accuracy, Precision, Recall, TP, TN
   * ROC Curve (Evaluation Matrix)
   * Confusion Matrix
5. **Bayesian Classifier**
   * Bayes Theorem
   * Classifier Terminology
   * Naive Bayes Theorem
   * Covariance Matrix
   * Univariate / Multivariate Densities
   * Risk Function
6. **Decision Tree**
   * Structure & Terminology (Root, Leaf, etc.)
   * Tree Design
   * Impurity Measures: Gini, Entropy
   * CART, ID3 (Multiclass)
   * Information Gain
   * Decision Tree Regularization
7. **Random Forest**
   * Ensemble Methods
   * Voting Classifier
   * Bagging
   * Random Subspace Method
   * OOB Instances
   * Boosting
   * Regression-based Random Forest
8. **SVM (Support Vector Machine)**
   * Terminology
   * Regularization
   * Parameters & Hyperparameters
   * Cost Function
   * Non-linear SVM
   * Kernel Techniques
   * Derivation of Optimal Margin
9. **K-Means**
   * Unsupervised Learning
   * Applications
   * Algorithm (with Pseudocode)
   * Cost Function
   * Elbow Method / Silhouette Score
10. **PCA (Principal Component Analysis)**
    * Eigenvectors & Eigenvalues
    * Eigen Decomposition
    * SVD & Types of SVD
    * Variance & Covariance
    * U, Sigma Matrix
    * Projection & Manifold Learning
    * Eigenface Problem
11. **Neural Networks**
    * Importance
    * Perceptron, LTU, TLU
    * Weights & Bias
    * Notation & Matrix Formulation
    * Forward Pass
    * Activation Functions
    * Gradient Descent
    * Backpropagation
    * Regularization
12. **CNN (Convolutional Neural Network)**
    * Architecture
    * Purpose
    * Kernels, Striding, Padding
    * Pooling
    * Example Architectures
    * Backpropagation in CNN
13. **Autoencoders**
    * Architecture
    * Loss Function
    * Variational Autoencoders (VAE)
    * Generative Models
14. **RNN (Recurrent Neural Network)**
    * Structure

</details>

<details>

<summary>Numerical problems</summary>

* 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.

</details>

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## DSA

<mark style="color:red;">**Paper will be MCQ only**</mark>

<details>

<summary>Topics</summary>

* <mark style="color:green;">**After Minor (weightage 70%)**</mark>
  * **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
* <mark style="color:green;">**Before Minor (weightage 30%)**</mark>
  * About Algo
  * Insertion Sort
  * Merge Sort
  * Recurrence Relation
  * Hashing
  * Binary Search Tree
  * Binary Search Tree Deletion

</details>

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## ODS

<details>

<summary>Topics</summary>

* **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

</details>

<details>

<summary>Patern</summary>

* **Fact-1**
  * 4 Subjective Questions (guess)
  * Remaining Objective Questions
* **Fact-2**
  * 4 Subjective Questions
  * Remaining Objective Questions

</details>

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## AI

<details>

<summary>Topics</summary>

* 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

</details>

<details>

<summary>Patern</summary>

* **Question Type:**
  * 3 Subjective Questions
  * Remaining Objective Questions
* **Difficulty Level:**
  * 25% Easy
  * 50% Medium
  * 25% Difficult

</details>

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