Course Number
705.601
Next Offered
Summer 2022
Primary Program
Artificial Intelligence
Location
Online
Mode of Study
Online

Machine Learning (ML) is the art of solving a computation problem using a computer without an explicit program. ML is now so pervasive that various ML applications such as image recognition, stock trading, email spam detection, product recommendation, medical diagnosis, predictive maintenance, cybersecurity, etc. are constantly used by organizations around us, sometimes without our awareness.In this course, we will rigorously apply machine learning techniques to real-world data to solve real-world problems. We will briefly study the underlying principles of diverse machine learning approaches such as anomaly detection, ensemble learning, deep learning with a neural network, etc. The main focus will be applying tool libraries from the Python-based Anaconda and Java-based Weka data science platforms to datasets from online resources such as Kaggle, UCI KDD, open source repositories, etc. We will also use Jupyter notebooks to present and demonstrate several machine learning pipelines.

Course Prerequisite(s)

EN.705.621 Introduction to Algorithms OR EN.605.621 Foundations of Algorithms OR EN.685.621 Algorithms for Data Science

Course Offering(s)

Waitlist Only

Applied Machine Learning

705.601.81
05/31/2022 - 08/23/2022
Semester
Summer 2022
Mode of Study
Online
Location
Online
Cost
$4,920.00
Textbook
Open

Applied Machine Learning

705.601.82
05/31/2022 - 08/23/2022
Semester
Summer 2022
Mode of Study
Online
Location
Online
Cost
$4,920.00
Textbook
Waitlist Only

Applied Machine Learning

705.601.83
05/31/2022 - 08/23/2022
Semester
Summer 2022
Mode of Study
Online
Location
Online
Cost
$4,920.00
Textbook
Open

Applied Machine Learning

705.601.81
08/29/2022 - 12/13/2022
Semester
Fall 2022
Mode of Study
Online
Location
Online
Cost
$4,920.00
Textbook
TBD
Open

Applied Machine Learning

705.601.82
08/29/2022 - 12/13/2022
Semester
Fall 2022
Mode of Study
Online
Location
Online
Cost
$4,920.00
Textbook
TBD