Syllabus

Class	Topics To Be Covered
Feb 18	Set Up Development Environment
Feb 23	Linear Regression
Feb 25	Naive Bayes Classifier
Mar 02	Logistic Regression, review of Linear Regression
Mar 04	K-nearest neighbors classifier
Mar 09	Support Vector Machines (Part 1)
Mar 11	Support Vector Machines (Part 2)
Mar 16	Gradient Descent, Stochastic Gradient Descent, & Regularization
Mar 18	Decision Trees
Mar 23	Regression Trees and Random Forests
Mar 25	Tree Boosting, Practicum on a Real Dataset
Mar 30	Artificial Neural Networks (Part 1)
Apr 01	Practicum 2: Tuning the Models
Apr 06	ANN wrap-up, Supervised learning Wrapup
Apr 08	Exploratory Analysis, SQL and NoSQL (Sonia)
Apr 13	Guest lectures
Apr 15	K-Means and Hierarchical Clustering
Apr 20	Practicum 3: Clustering
Apr 22	Dimensionality reduction through PCA
Apr 27	Spark, Hadoop and MapReduce
Apr 29	Built-in buffer for work on final projects
May 04	Final Presentations (Part 1)
May 06	Final Presentations (Part 2)