Machine Learning -3 Syllabus

 MACHINE LEARNING Syllabus:

UNIT-1

Introduction: Brief Introduction to Machine Learning, Abstraction and Knowledge Representation, Types of Machine Learning Algorithms, Definition of learning systems, Goals and applications of machine learning, Aspects of developing a learning system, Data Types, training data, concept representation, function approximation.

Data Pre-processing: Definition, Steps involved in pre-processing, Techniques

UNIT-2

Performance measurement of models: Accuracy, Confusion matrix, TPR, FPR, FNR, TNR, Precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC.

Supervised Learning1: Linear Regression, Multiple Variable Linear Regression, Naïve Bayes Classifiers, Gradient Descent, Multicollinearity, Bias-Variance trade-off.

UNIT-3

Supervised Learning2: Regularization, Logistic Regression, Squashing function, KNN, Support Vector Machine.

Decision Tree Learning: Representing concepts as decision trees, Recursive induction of decision trees, picking the best splitting attribute: entropy and information gain, searching for simple trees and computational complexity, Occam's razor, overfitting, noisy data, and pruning. Decision Trees – ID3-CART-Error bounds.

 

UNIT-4

Unsupervised Learning: K-Means, Customer Segmentation, Hierarchical clustering, DBSCAN, Anomaly Detection, Local Outlier Factor, Isolation Forest, Dimensionality Reduction, PCA, GMM, Expectation Maximization.

UNIT-5

Ensemble Models: Ensemble Definition, Bootstrapped Aggregation (Bagging) Intuition, Random Forest and their construction, Extremely randomized trees, Gradient Boosting, Regularization by Shrinkage, XGBoost, AdaBoost.

 

TEXT BOOKS:

1.      Machine Learning – Tom M. Mitchell, - MGH

2.      Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Prentice Hall of India, Third Edition 2014.

3.      The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani & Jerome Friedman, Springer Verlag, 2001.

REFERENCES: -

1.      Machine Learning, SaikatDutt, Subramanian Chandramouli, Amit Kumar Das, Pearson, 2019.

2.      Stephen Marsland, “Machine Learning -An Algorithmic Perspective”, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.

3.      Application of machine learning in industries (IBM ICE Publications).

e-Resources:

1.      Andrew Ng, “Machine Learning Yearning” https://www.deeplearning.ai/machinie-learning

2.      Shai Shalev-Shwartz, Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press. https://www.cs.huji.ac.il/w~shais/UnderstaningMachineLearning/index.html