Machine Learning PCCST503 Semester5 KTU CS 2024 Scheme - Dr Binu V P

About Me - Dr Binu V P

Syllabus

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

Do the programs to better understand the concept. Machine 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 (Least Absolute Shrinkage and Selection Operator)
• 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- advantages and limitations
• Neural Network - Multilayer feed-forward network
• Activation Functions (Sigmoid, ReLU, Tanh etc..)
• Back Propagation Algorithm

Module IV

Unsupervised Learning

• Clustering
• Intruduction to Clustering in Machine Learning
• Applications of Clustering
• Clustering Vs Classification
• Partitional Clustering and K-means Clustering
• Numerical Problems- K means Clustering
• Elbow Method for Finding the Optimal Value of K
• Determining Optimal K using Silhouette Method
• Comparison of Silhouette Method and Elbow Method
• Hierarchical Clustering - Agglomerative Clustering
• Example  Problems - Agglomarative Clustering (Single Link)
• Comparison of Agglomerative Clustering and K-Means Clustering

Dimensionality Reduction 

• Principal Component Analysis
• Multidimensional Scaling

Resampling Methods 

• Bootstrapping
• Cross Validation

Ensemble methods 

• Bagging
• Boosting

Practical Aspects

• Bias-Variance tradeoff