Unit I : Introduction to Machine Learning Classic and adaptive machines, Machine learning matters, Beyond machine learning-deep learning and bio inspired adaptive systems, Machine learning and Big data. Important Elements of Machine Learning - Data formats, Learnability, Statistical learning approaches, Elements of information theory. (Chapter - 1) Unit II : Feature Selection Scikit- learn Dataset, Creating training and test sets, managing categorical data, Managing missing features, Data scaling and normalization, Feature selection and Filtering, Principle Component Analysis(PCA)-non negative matrix factorization, Sparse PCA, Kernel PCA. Atom Extraction and Dictionary Learning. (Chapter - 2) Unit III : Regression Linear regression - Linear models, A bi-dimensional example, Linear Regression and higher dimensionality, Ridge, Lasso and ElasticNet, Robust regression with random sample consensus, Polynomial regression, Isotonic regression, Logistic regression - Linear classification, Logistic regression, Implementation and Optimizations, Stochastic gradient descendent algorithms, Finding the optimal hyper-parameters through grid search, Classification metric, ROC Curve. (Chapter - 3) Unit IV : Naïve Bayes and Support Vector Machine Bayes' Theorem, Naïve Bayes. Classifiers, Naïve Bayes in Scikit- learn- Bernoulli Naïve Bayes, Multinomial Naïve Bayes, and Gaussian Naïve Bayes. Support Vector Machine(SVM) - Linear Support Vector Machines, Scikit - learn implementation - Linear Classification, Kernel based classification, Non - linear Examples. Controlled Support Vector Machines, Support Vector Regression. (Chapter - 4) Unit V : Decision Trees and Ensemble Learning Decision Trees - Impurity measures, Feature Importance. Decision Tree Classification with Scikit-learn, Ensemble Learning-Random Forest, AdaBoost, Gradient Tree Boosting, Voting Classifier. Clustering Fundamentals - Basics, K-means : Finding optimal number of clusters, DBSCAN, Spectral Clustering. Evaluation methods based on Ground Truth- Homogeneity, Completeness, Adjusted Rand Index. Introduction to Meta Classifier : Concepts of Weak and eager learner, Ensemble methods, Bagging, Boosting, Random Forests. (Chapter - 5) Unit VI : Clustering Techniques Hierarchical Clustering, Expectation maximization clustering, Agglomerative Clustering- Dendrograms, Agglomerative clustering in Scikit- learn, Connectivity Constraints. Introduction to Recommendation Systems - Naïve User based systems, Content based Systems, Model free collaborative filtering-singular value decomposition, alternating least squares. Fundamentals of Deep Networks - Defining Deep learning, common architectural principles of deep networks, building blocks of deep networks. (Chapter - 6)
