Subject Details
Dept     : AIML
Sem      : 4
Regul    : 2023
Faculty : Ms S Rajarajeswari
phone  : 9488704705
E-mail  : rajarajeswari.s.aiml@snsct.org
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Lecture Notes

UNIT 1:
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Machine Learning process- preliminaries, testing Machine Learning algorithms
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Machine Learning–Types of Machine Learning
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turning data into Probabilities and Statistics for Machine Learning - Probability theory – Probability Distributions
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Decision Theory – Bias, Variance and Tradeoff.
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Decision Theory – Bias, Variance and Tradeoff.
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turning data into Probabilities and Statistics for Machine Learning - Probability theory – Probability Distributions
UNIT 2:
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Logistic Regression: - SVM and Hyperparameter tuning - Implementing SVM using scikit-learn
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Classification and Regression
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Linear Regression
UNIT 3:
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Random Forests, – Ensemble - Boosting - AdaBoost and Gradient Boosting
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Random Forests, – Ensemble - Boosting - AdaBoost and Gradient Boosting
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Random Forests, – Ensemble - Boosting - AdaBoost and Gradient Boosting
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Random Forests, – Ensemble - Boosting - AdaBoost and Gradient Boosting
UNIT 4:
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Clustering- K-means – EM Algorithm- Mixtures of Gaussians
UNIT 5:
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Model Free Learning - Temporal Difference Learning
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Introduction - Single State Case - Elements of Reinforcement Learning
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Model Free Learning - Temporal Difference Learning