Components of learning –learning models –geometric models –probabilistic models –logic models –grouping and grading –learning versus design –types of learning –supervised –unsupervised –reinforcement –theory of learning –feasibility of learning–error and noise –training versus testing –theory of generalization –generalization bound –approximation-generalization tradeoff –bias and variance –learning curve
Linear classification –univariate linear regression –multivariate linear regression –regularized regression –Logistic regression –perceptrons –multilayer neural networks –learning neural networks structures –support vector machines –soft margin SVM –going beyond linearity –generalization and overfitting –regularization –validation
Nearest neighbormodels –K-means –clustering around medoids –silhouttes –hierarchical clustering –k-d trees –locality sensitive hashing–non-parametric regression –ensemble learning –bagging and random forests –boosting –meta learning
Decision trees –learning decision trees –ranking and probability estimation trees –regression trees –clustering trees –learning ordered rule lists –learning unordered rule lists –descriptive rule learning –association rule mining –first-order rule learning
Introduction to scikit-learn library – supervised learning – k nearest neighbors – linear regressions – support vector machines – support vector regression
Reference Book:
K. P. Murphy ―Machine Learning: A probabilistic perspective‖, MIT Press, 2012. M. Mohri, A. Rostamizadeh, and A. Talwalkar ―Foundations of Machine Learning‖, MIT Press, 2012 D. Barber ―Bayesian Reasoning and Machine Learning‖, Cambridge University Press, 2012
Text Book:
Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, ―Learning from Data, AML Book Publishers, 2012. P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data‖, Cambridge University Press, 2012.
