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
About Data Wrangling
- DATA WRANGLING UNIT-1 (A) Introduction to Data Wrangling PPTs
- DATA WRANGLING UNIT-1(C) Data Meant to Be Read by Machines PPTs
- DATA WRANGLING UNIT-2 (A) Working with Excel Files PPTs
- DATA WRANGLING UNIT-2 (B) Working with PDFs PPTs
- DATA WRANGLING UNIT-2 (C) Acquiring and Storing Data PPTs
- DATA WRANGLING UNIT-3 (A) Data Cleanup PPTS
- DATA WRANGLING UNIT-3 (B) Data Cleanup- Standardizing and Scripting PPTs
- DATA WRANGLING UNIT-4(A) Data Exploration and Analysis PPTs
- DATA WRANGLING UNIT-4(B) Presenting Your Data PPTs
- DATA WRANGLING UNIT-5 (A) WEB SCRAPING PPT
- DATA WRANGLING UNIT-5(B) Advanced Web Scraping PPTs
- CSV File Reading
- JSON File Reading
- XML File Reading
- Convert Python dictionary object (sort by key) to JSON data. Print the object members with indent level 4
- Read a given CSV file as a Dictionary.
- Read each row from a given csv file and print a list of strings
- Excel File Reader
- Data Wrangling - Data Cleanup