Machine Learning PCCST503 Semester 5 KTU CS 2024 Scheme - Dr Binu V P

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Syllabus

Model Question Paper Machine Learning PCCST503 Semester 5 KTU CS 2024 Scheme

Do the programs to better understand the concept. Machiine Learning Lab PCCSL508

Module -I

Introduction to Machine Learning

• Introduction to ML.
• Distinguishing Machine Learning from Traditional Programming.
• Machine Learning Paradigms.
• Supervised. 
• UnSupervised.
• Semi-Supervised.
• Reinforcement.
• Stages of Machine Learning: From Data Collection to Deployment

Parameter Estimation

• Introduction to Parameter Estimation in Machine Learning.
• Maximum Likelyhood Estimation.(MLE).
• Maximum A Posteriori Estimation.(MAP).
• Numerical Problems MLE and MAP.

Supervised Learning

• Feature Representation and Problem Formulation.
• Role of loss functions and optimization.

 Regression

• Introduction to Regression  
• Linear regression with one variable.  (  lab experiment )
• Example Problems- simple linear regression - University Questions
• Evaluation Metrics for Linear Regression
• Linear Regression with multiple variables  ( lab experiment )
• Gradient Descent (GD) 
• Simple Linear Regression using Gradient Descent ( lab experiment )
• Multivariate Linear Regression using Gradient Descent ( lab experiment)

Module -II

Classification

• Understanding Machine Learning Classification Algorithms
• Logistic  Regression ( lab experiment )
• Types of Logistic Regression
• Baye's Theorem in Machine Learning
• Baye's Theorem Examples
• Naive Bayes Classifier ( lab experiment )
• Naive Bayes Classifier - Example Problems
• K-Nearest Neighbors (KNN)  ( lab experiment )
• Decision Tree - ID3 Algorithm (Iterative Dichotomiser3 Algorithm ) ( lab experiment )

Building Better Models and Evaluation   

• Training , Validation and Testing
• Generalization Vs Overfitting
• Bias Variance Trade-off
• Regularization Techniques
• Regularization
• LASSO and Ridge
• Optimization to Probabilistic Modeling
• Classification Evaluation Measures
• Confusion Matrix- Accuracy , Precision and  Recall
• Type I and Type II Errors
• Support
• Numerical Problems- Classification Measures
• F-measures
• Receiver Operating Characteristic Curve(ROC), Area Under Curve (AUC).

Module III

SVM

•  Introduction to SVM
•   Linear SVM

Neural Networks

• Introduction to ANN
• Perceptron
• AND Gate using Perceptron
• OR Gate using Perceptron
• NAND Gate using Perceptron
• XOR problem- how it is solved using multi layer perceptron
• Applications of the Perceptron- advatages and limitations
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