ML SYLLABUS AND PROMPTS

April 10, 2026

ML SYLLABUS AND PROMPTS

ML SYLLABUS

UNIT-I: Introduction to Machine Learning: Evolution of Machine Learning, Paradigms for ML, Learning by Rote, Learning by Induction, Reinforcement Learning, Types of Data, Matching, Stages in Machine Learning, Data Acquisition, Feature Engineering, Data Representation, Model Evaluation, Model Prediction, Search and Learning, Data Sets.

UNIT-II: Nearest Neighbor-Based Models: Introduction to Proximity Measures, Distance Measures, Non-Metric Similarity Functions, Proximity Between Binary Patterns, different Classification Algorithms Based on the Distance Measures, K-Nearest Neighbor Classifier, Radius Distance Nearest Neighbor Algorithm, KNN Regression, Performance of Classifiers, Performance of Regression Algorithms. Regulation D23

UNIT-III: Models Based on Decision Trees: Decision Trees for Classification, Impurity Measures, Properties, Regression Based on Decision Trees, Bias–Variance Trade-off, Random Forests for Classification and Regression. The Bayes Classifier: Introduction to the Bayes Classifier, Bayes’ Rule and Inference, The Bayes Classifier and its Optimality, Multi-Class Classification, Class Conditional Independence and Naive Bayes Classifier (NBC)

UNIT-IV: Linear Discriminants for Machine Learning: Introduction to Linear Discriminants, Linear Discriminants for Classification, Perceptron Classifier, Perceptron Learning Algorithm, Support Vector Machines, Linearly Non-Separable Case, Non-linear SVM, Kernel Trick, Logistic Regression, Linear Regression, Multi-Layer Perceptron’s (MLPs), Backpropagation for Training an MLP.

UNIT-V: Clustering: Introduction to Clustering, Partitioning of Data, Matrix Factorization, Clustering of Patterns, Divisive Clustering, Agglomerative Clustering, Partitional Clustering, K-Means Clustering, Soft Partitioning, Soft Clustering, Fuzzy C-Means Clustering, Rough Clustering, Rough K-Means Clustering Algorithm, Expectation Maximization-Based Clustering, Spectral Clustering.

ML ALL UNITS PROMPTS LINKS

ML UNIT-5 PROMPT

ML UNIT-4 PROMPT

ML UNIT-3 PROMPT

ML UNIT-1 PROMPT

Act as a Machine Learning subject expert and experienced university-level professor.

I am preparing for my semester-end exams, and this is my final revision, so teach me in a very clear, detailed, and exam-oriented manner.

Assume I am a beginner, so explain everything in a simple, step-by-step way, but also ensure the content is deep enough for scoring full marks in exams.

📘 SUBJECT: Machine Learning
📗 UNIT – I: Introduction to Machine Learning

🔹 TOPICS TO COVER (ONE BY ONE):

Evolution of Machine Learning
Paradigms for Machine Learning
Learning by Rote
Learning by Induction
Reinforcement Learning
Types of Data
Matching
Stages in Machine Learning
Data Acquisition
Feature Engineering
Data Representation
Model Evaluation
Model Prediction
Search and Learning
Data Sets

🔹 INSTRUCTIONS:

• Explain ONE TOPIC AT A TIME only
• After completing each topic, wait for my instruction to continue

🔹 FOR EACH TOPIC, FOLLOW THIS STRUCTURE:

Introduction (Simple Explanation)
Definition (Exam-ready)
Detailed Explanation (Step-by-step)
Real-life Example
Diagram / Flowchart (VERY IMPORTANT)
- Use clear text-based diagrams
- Add explanation for the diagram
Key Points / Keywords (for revision)
Possible Exam Questions (2–3)

🔹 SPECIAL REQUIREMENTS:

• Use very simple language + clear logic
• Include diagrams wherever needed for better understanding
• Make explanations easy to remember in exams
• Highlight important terms and concepts
• Avoid unnecessary theory — focus on what helps in exams

🔹 OUTPUT STYLE:

• Use headings and subheadings
• Use bullet points where needed
• Keep answers neat, structured, and readable

🔹 IMPORTANT RULE:

🚫 Do NOT explain all topics at once
✅ Start ONLY when I say the topic name
⏳ After each topic, wait for my next instruction

👉 Start ONLY when I say:

Example: “Start Evolution of Machine Learning”

ML UNIT-2 PROMPT