Machine Learning
Syllabus
Unit I:
The
Machine Learning Landscape: What Is Machine Learning? Why Use
Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised
Learning, Batch and Online Learning, Instance-Based Versus Model-Based
Learning, Main Challenges of Machine Learning, Insufficient Quantity of
Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant
Features, Overfitting the Training Data, Underfitting the Training Data,
Stepping Back, Testing and Validating.
Unit II:
Classification:
Training a Binary Classifier, Performance Measures, Measuring Accuracy Using
Cross-Validation, Confusion Matrix, Precision and Recall, Precision/Recall
Tradeoff, The ROC Curve, Multiclass Classification, Error Analysis, Multilabel
Classification, Multi Output Classification. k-NN Classifier.
Unit III:
Support
Vector Machines: Linear SVM Classification, Soft Margin
Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding
Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM
Regression, Under the Hood, Decision Function and Predictions, Training
Objective, Quadratic Programming, The Dual Problem, Kernelized SVM, Online
SVMs.
Unit IV:
Decision
Trees: Training and Visualizing a Decision Tree, Making
Predictions, Estimating Class Probabilities, The CART Training Algorithm,
Computational Complexity, Gini Impurity or Entropy? Regularization
Hyperparameters, Regression
Ensemble
Learning and Random Forests: Voting Classifiers,
Bagging and Pasting, Bagging and Pasting in Scikit-Learn, Out-of-Bag
Evaluation, Random Patches and Random Subspaces, Random Forests, Extra-Trees,
Feature Importance, Boosting, AdaBoost, Gradient Boosting, Stacking.
Unit V:
Dimensionality
Reduction: The Curse of Dimensionality, Main Approaches for
Dimensionality Reduction, Projection, PCA.
Clustering:
How does clustering work: finding similarities using distances, Euclidean
distance and other distance metrics. k-Means Clustering: Plotting
customers with their segments, normalizing features, cluster centres and
interpreting the Clusters. Hierarchical Clustering.
Textbooks:
1. Géron,
Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow:
Concepts, tools, and techniques to build intelligent systems. O'Reilly Media,
2019.
2. Pradhan,
Manaranjan, and U. Dinesh Kumar. Machine Learning using Python. Wiley, IIM
Bangalore, 2019.
References:
1.
Introduction
to Machine Learning, Ethem Alpaydin 2nd Edition, MIT Press 2000
2.
Machine
Learning, Tom M. Mitchell, McGraw Hill, 1997, ISBN: 0-07-042807-7.
