Definition of learning systems-Goals and applications of machine learning- Types of Machine Learning- - Machine Learning Process-Terminology-Weight Space-The Curse of Dimensionality- Testing Machine Learning Algorithms.
Regression: Linear Regression –Parametric Models- Multivariate Regression. Classification: Bayesian Decision Theory-parametric and non-parametric methods- Multivariate Classification-Logistic Regression- K-Nearest Neighbor classifier. Decision Tree based methods for classification and Regression- Ensemble methods.
Introduction-Clustering-K-means clustering, EM algorithm, Hierarchical Clustering- Principal Component Analysis- Probabilistic PCA.
The Brain and The Neuron-Neural Networks-Perceptron-Training the perceptron -Perceptron Learning Algorithm- Multilayer Perceptron- Back Propagation -Dimensionality Reduction.
Convolutional Networks, Recurrent Neural Networks, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, Applications – Speech Recognition.
Reference Book:
Stephen Marshland, “Machine Learning: An Algorithmic Perspectiveâ€, Chapman & Hall/CRC 2009. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, “Foundations of Machine Learningâ€, MIT Press (MA) 2012.
Text Book:
Ethem Alpaydin, “Introduction to Machine Learningâ€, 4th edition, MIT Press, March 2020. Mitchell, Tom, “Machine Learningâ€, New York, McGraw-Hill, First Edition, 2013. Ian Good Fellow,Yoshua Bengio, Aaron Courville, “Deep Learningâ€, MIT Press Book, 2016.
