About Machine Learning 2

Machine Learning 

• Introduction
• Concept Learning and the General to Specific Ordering
• Decision Tree Learning
• Artificial Neural Networks
• Bayesian Learning
• Instance-Based Learning
• Genetic Algorithms
• Learning Sets of Rules
• Analytical Learning
• Reinforcement Learning

👉Machine Learning 2 Syllabus

UNIT-1 : Introduction & Concept Learning and the General to Specific Ordering

Introduction- Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning.

Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm.

👉Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes

👉Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs

👉Machine Learning 2- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs

👉Machine Learning 2 - UNIT-1 Questions

UNIT-2: Decision Tree Learning & Artificial Neural Networks

Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.

Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.

👉Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning

👉Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs

👉Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes

👉Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs

👉Machine Learning2 - UNIT 2 : Decision Tree and Artificial Neural Network Questions

UNIT-3: Bayesian Learning & Instance-Based Learning

Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.

Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.

👉Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes

👉Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs

👉Machine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs

👉Machine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs

👉Machine Learning2- UNIT-3 Questions

UNIT-4: Genetic Algorithms & Learning Sets of Rules

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL

👉Machine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs

👉Machine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs

 👉Machine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs

👉Machine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs

👉Machine Learning2: UNIT-4 Questions

UNIT-5: Analytical Learning & Reinforcement Learning

Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.

Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

👉Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

👉Machine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs

👉Machine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs

👉Machine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs

👉Machine Learning2: UNIT-5 Questions

Text Books:

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

2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

Reference Books:

1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ. Press.

2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.

 

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

MACHINE LEARNING UNIT - 2 (A) NOTES (Decision Tree Learning)

MACHINE LEARNING UNIT – 2 (A) PPT's (Decision Tree Learning)