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 : Naive Bayes and Support Vector Machine Bayes Theorem, Naive Bayes Classifiers, Naive Bayes in Scikit - learn - Bernoulli Naive Bayes, Multinomial Naive Bayes and Gaussian Naive 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 : Naive User based systems, Content based systems, Model free collaborative filtering - singular value deposition, alternating least squares. Fundamentals of Deep Networks : Defining Deep learning, common architectural principles of deep networks, building blocks of deep networks. (Chapter - 6)
