Machine Learning 1 Syllabus

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.