Monday Class Options:
 
COMSC 415- Machine Learning: 12:00pm-12:50pm
Machine learning is the study of how to build computer systems that learn from data in order to make predictions, recognize patterns, and organize information. This course will explore both the underlying mathematical theory and practical application of methods for machine learning. Topics include supervised and unsupervised learning, dimensionality reduction, support vector machines, decisions trees, clustering, neural networks. In addition, students will use advanced deep learning techniques to build models using large scale data. Potential implementations include image recognition, signal processing, time series forecasting, recommender systems, reinforcement learning, computer vision, and sentiment analysis. Multiple case studies will be drawn from real-word applications including business, science, engineering, bioinformatics, healthcare, political science, epidemiology, and public health. (3 credits) Alternate Spring

MATH 255 - Scientific Computing and Data Visualization: 1:00pm-1:50pm
This course prepares students to use specialized computing software for mathematical and scientific problems solving, exploration, and visualization. Students will learn how to take advantage of the capabilities of scientific computing software in a variety of mathematical and modeling situations. This includes understanding the fundamental data structures such as vectors, matrices, multi-dimensional arrays, and data frames; implementing basic and advanced data visualization techniques; performing numerical computations for solving systems of equations, optimization problems, interpolation problems, integral equations, and differential equations. In addition, students will learn how to use latest cutting-edge technologies to access real-world datasets from different resources and perform large-scale data visualization and simulations.

Thursday Class Options:

MGMT 200 - Management Principles: 11:00 AM-12:20 PM
Analysis of general management, organizations, and organizational behavior. Emphasizes the managers accountability for efficient and effective performance, which includes responsibility for making work organizations more fit for human habitation.