With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
Week 1: Introductory Topics Week 2: Linear Regression and Feature Selection Week 3: Linear Classification Week 4: Support Vector Machines and Artificial Neural Networks Week 5: Bayesian Learning and Decision Trees Week 6: Evaluation Measures Week 7: Hypothesis Testing Week 8: Ensemble Methods Week 9: Clustering Week 10: Graphical Models Week 11: Learning Theory and Expectation Maximization Week 12: Introduction to Reinforcement Learning
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.