Course

Module-1

Introduction to Machine Learning–Data and Features–Machine Learning Pipeline : Data Preprocessing : Standardization, Normalization, Missing data problem, Data imbalance problem
Data visualization – Setting up training, development and test sets – Cross validation – Problem of Overfitting, Biasvs Variance–Evaluation measures–Different types of machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Generative Learning and adversarial learning.

Module-2

Supervised learning – Regression: Linear regression, logistic regression – Classification: K-Nearest Neighbor, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, Perceptron, Error analysis.

Module-3

Unsupervised learning – Clustering: K-means, Hierarchical, Spectral, subspace clustering, Gaussian Mixture Model, Hidden Markov Model, Parameter Estimation : MLE and Bayesian Estimate, Expectation Maximization, Dimensionality Reduction Techniques, Principal component analysis, Linear Discriminant Analysis.

Module-4

Introduction to Neural Networks, Reinforcement learning and generative learning.

Lab Session

• Perform data preprocessing (missing values, standardization, normalization, data imbalance) and data visualization; implement cross-validation using Python with pandas, scikit-learn, and matplotlib.
• Implement regression (linear, logistic) and classification (KNN, Naïve Bayes, Decision Tree, Random Forest, SVM, Perceptron) models with error analysis using scikit-learn in Python.
• Apply clustering (K-means, hierarchical, spectral), dimensionality reduction (PCA, LDA), and parameter estimation (MLE, Bayesian, EM for Gaussian Mixture Models) using scikit-learn in Python.
• Develop basic neural networks, reinforcement learning, and generative learning models using TensorFlow or PyTorch in Python.

• CO1: Apply pre-processing techniques to prepare the data for machine learning applications
• CO2:Implement supervised machine learning algorithms for different datasets
• CO3:Implement unsupervised machine learning algorithms for different datasets
• CO4:Analyze the error to improve the performance of the machine learning models

• Andrew Ng, Machine learning yearning,URL:http://www.org/(96)139(2017).
• Kevin P.Murphey. Machine Learning,a probabilistic perspective. The MIT Press Cambridge, Massachusetts,2012.
• Christopher M Bishop.Pattern Recognition and Machine Learning.Springer2010
• Duda,PeterE.Hart,David G.Stork.Pattern Classification.Wiley,Second Edition;2007
• Sutton,Richard S., and Andrew G.Barto. Reinforcementl earning : An introduction. MITpress,2018.