R_23 : Machine Learning - Unit wise topics (Very Important, Important, Tips)

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UNIT–I: Introduction to Machine Learning

Very Important

  • Machine Learning definition & types (Supervised vs Unsupervised)
  • Learning paradigms:
    • Learning by Rote
    • Learning by Induction
    • Reinforcement Learning
  • Stages in Machine Learning Pipeline
  • Feature Engineering & Data Representation
  • Model Evaluation techniques (Accuracy, Precision, Recall basics)

Important

  • Types of Data (Structured, Unstructured)
  • Model Selection vs Model Learning
  • Training vs Testing vs Validation

Quick Topics

  • Data acquisition basics
  • Data sets (training/test split)

Exam Tip:
 Expect 2–5 mark theory questions + 10 mark ML pipeline diagram

UNIT–II: KNN & Distance-Based Models

Very Important

  • K-Nearest Neighbor (KNN) Algorithm (FULL)
    • Steps of algorithm
    • Numerical problems (very important 🔥)
  • Distance Measures
    • Euclidean
    • Manhattan
    • Minkowski
  • KNN for Classification vs Regression

Important

  • Radius-based nearest neighbor
  • Performance of classifiers vs regression

Quick Topics

  • Binary similarity measures
  • Non-metric similarity functions

Exam Tip:
Numerical problem on KNN is almost guaranteed

UNIT–III: Decision Trees & Bayes

Very Important

  • Decision Tree Algorithm (ID3 basics)
  • Impurity Measures
    • Entropy
    • Gini Index
  • Information Gain (Numerical problems 🔥)
  • Naive Bayes Classifier (Very Important)
    • Bayes theorem
    • Conditional probability
    • Numerical problems

Important

  • Bias-Variance Tradeoff
  • Random Forest (basic working)

Quick Topics

  • Multi-class classification
  • Independence assumption in Naive Bayes

Exam Tip:
 This unit is very high weightage
 Expect 10–15 marks (numerical + theory)

UNIT–IV: Linear Models & Neural Networks

 Very Important

  • Perceptron Algorithm
  • Support Vector Machine (SVM)
    • Linear vs Non-linear
    • Kernel Trick
  • Logistic Regression (very important for theory)
  • Backpropagation Algorithm (steps + diagram 🔥)

Important

  • Linear Regression (basic equation & idea)
  • Multi-Layer Perceptron (MLP structure)

Quick Topics

  • Linearly separable vs non-separable case

Exam Tip:
 Expect diagram-based + algorithm explanation questions

UNIT–V: Clustering

Very Important

  • K-Means Clustering (VERY IMPORTANT 🔥🔥)
    • Steps
    • Numerical problems
  • Hierarchical Clustering
    • Agglomerative vs Divisive
  • Fuzzy C-Means Clustering (concept + formula)

Important

  • Soft vs Hard clustering
  • Expectation Maximization (EM)

Quick Topics

  • Spectral clustering
  • Rough clustering

Exam Tip:
 K-Means numerical is almost guaranteed question

 FINAL EXAM STRATEGY (VERY IMPORTANT)

 MUST PREPARE (80% marks coverage)

  • KNN (Unit 2)
  • Decision Trees + Entropy (Unit 3)
  • Naive Bayes (Unit 3)
  • SVM + Perceptron (Unit 4)
  • K-Means (Unit 5)
  • ML Pipeline (Unit 1)

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