Machine Learning (CS 501) - Semester 5 BTech IT at IETV

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Overview

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Why it matters

ML is THE future. ChatGPT, recommendation systems, fraud detection, self-driving cars — all ML. This is the most in-demand skill in tech. Master this, earn ₹30-50 LPA at AI startups.

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Placement relevance

HIGHEST demand field. ML Engineer roles at Google, Microsoft, Amazon. Data Scientist positions everywhere. Kaggle competitions boost resume. Every product company wants ML talent.

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Prerequisites for

Deep Learning · Natural Language Processing · Computer Vision · AI Research · Data Science · Reinforcement Learning

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Recommended books

Pattern Recognition and Machine Learning by Christopher Bishop · Hands-On Machine Learning by Aurélien Géron · Deep Learning by Goodfellow, Bengio, and Courville · Machine Learning by Tom Mitchell

Curriculum — 4 Units

U1

Unit 1 · 7 Topics · 0% complete

Supervised Learning

⚡ Key Formulae

Linear Regression:y = mx + c; Cost = (1/2m)Σ(h(x)-y)²

Gradient Descent:θ := θ - α(∂J/∂θ)

Sigmoid:σ(z) = 1/(1 + e^(-z))

Linear Regression

Logistic Regression

Decision Trees

Random Forest

SVM

Naive Bayes

KNN

U2

Unit 2 · 6 Topics · 0% complete

Unsupervised Learning

⚡ Key Formulae

K-Means:Minimize: Σ ||x - μ_k||²

PCA:Maximize variance: v^T Σ v

K-Means Clustering

Hierarchical Clustering

DBSCAN

PCA

Dimensionality Reduction

Anomaly Detection

U3

Unit 3 · 6 Topics · 0% complete

Neural Networks & Deep Learning

⚡ Key Formulae

Perceptron:y = σ(Σw_i·x_i + b)

ReLU:f(x) = max(0, x)

Dropout:Randomly drop neurons with probability p

Perceptron

Activation Functions

Backpropagation

CNN Basics

RNN Basics

Overfitting & Regularization

U4

Unit 4 · 6 Topics · 0% complete

Model Evaluation

⚡ Key Formulae

Accuracy:(TP + TN) / Total

Precision:TP / (TP + FP)

Recall:TP / (TP + FN)

F1 Score:2 × (Precision × Recall) / (Precision + Recall)

Train/Test Split

Cross-Validation

Confusion Matrix

Precision, Recall, F1

ROC Curve

Bias-Variance Tradeoff

Previous Year Questions

Unit 12023 · End Semester10 marks

Implement Linear Regression from scratch in Python. Given dataset with house sizes and prices, fit a model and predict price for a new house. Calculate MSE.

Unit 22023 · End Semester8 marks

Apply K-Means clustering on given 2D dataset with k=3. Show iterations, centroids update, and final clusters. Plot the result.

Unit 42022 · End Semester6 marks

Given confusion matrix: TP=50, TN=40, FP=10, FN=5. Calculate Accuracy, Precision, Recall, and F1 Score. Which metric is best for imbalanced data?

Exam Strategy

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Code in Python, not pseudocode

Exams ask for actual Python code using sklearn. Practice: model.fit(), model.predict(), train_test_split(). Import statements matter.

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Understand math, don't just memorize

Gradient descent, cost functions, backpropagation — understand WHY they work. Questions ask 'Derive the update rule' not just 'What is the formula'.

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Always plot results

Decision boundaries, loss curves, confusion matrices — visual answers earn extra marks. Use matplotlib even in exams if allowed.

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Bias-Variance is always asked

Understand overfitting (high variance) vs underfitting (high bias). Know solutions: regularization, more data, cross-validation.

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