UNIT-5 :Introduction to Data Science: Functional Programming,
JSON and XML in Python, NumPy with Python, Pandas.
UNIT-3:
- Models Based on Decision Trees PPT Notes
- Decision Trees for Classification, Impurity Measures, Properties, Regression Based on Decision Trees, Bias–Variance Trade-off, Random Forests for Classification and Regression. The Bayes Classifier: Introduction to the Bayes Classifier, Bayes’ Rule and Inference, The Bayes Classifier and its Optimality, Multi-Class Classification, Class Conditional Independence and Naive Bayes Classifier (NBC)
UNIT-4:
- Linear Discriminants for Machine Learning: Notes
- Introduction to Linear Discriminants, Linear Discriminants for Classification, Perceptron Classifier, Perceptron Learning Algorithm, Support Vector Machines, Linearly Non-Separable Case, Non-linear SVM, Kernel Trick, Logistic Regression, Linear Regression, Multi-Layer Perceptron's (MLPs), Backpropagation for Training an MLP.
UNIT-5:
Previous Question papers:
Text Books :
- “Machine Learning Theory and Practice”, M N Murthy, V S Ananthanarayana, Universities Press (India), 2024.
- Tom M. Mitchell, “Machine Learning’, MGH, 2017.
UNIT-1
UNIT-2
