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