Machine Learning | CS PPT +Worksheets +Activity + Mcq Quiz + Answers_Marking Scheme

Machine Learning | CS PPT +Worksheets +Activity + Mcq Quiz + Answers_Marking Scheme

01 Lecture PPT13 slidesWhat is ML? → Types (Supervised/Unsupervised/Reinforcement) → ANN Structure & Neuron Computation → Activation Functions (ReLU, Sigmoid, Softmax) → Back-Propagation (5-step flow + gradient descent) → Linear Regression (worked example + R²) → Logistic Regression (sigmoid curve + decision boundary) → Overfitting/Underfitting + Evaluation Techniques → Reasons for ML → Spam Classifier worked example → Practice Qs → Summary

02 Worksheet80 marks14 questions across 5 sections — ML types, neuron calculations, activation functions, weight counting, full back-prop description, gradient descent trace, linear/logistic regression calculations, confusion matrix metrics, ML system design

03 Activity4 tasksHuman Neural Network simulation (physical forward pass + back-prop) → Regression Data Investigation (fit linear & logistic models by hand) → ML System Design Challenge (4 real-world problems, design full pipeline) → Ethics Debate + Amazon/Hospital ML bias case studies

04 MCQ Quiz21 slides20 questions + Answer Key — covers all topics from supervised vs unsupervised, neuron weighted sums, ReLU vs sigmoid, back-prop gradients, learning rate effects, R² interpretation, sigmoid probability, overfitting, dropout

05 Answers & Marking SchemeTeacher copyFull worked numerical solutions, mark bands for extended answers, complete confusion matrix worked solutions, MCQ key with explanations, section-by-section mark allocation — Teacher Copy only