Artificial Intelligence (AI) Certificate

This course assumes that the student is familiar with the fundamentals of algebraic concepts, expressions, equations, and inequalities. Topics include functions and their graphs; polynomial and rational functions; exponential and logarithmic functions; systems of equations and inequalities; partial fractions; operations with matrices; arithmetic and geometric sequences and series.

This course provides an introduction to the field of Reinforcement Learning with an emphasis on Deep Reinforcement Learning. Topics cover may include, but are not limited to: Markov decision processes, value-based methods, Deep Q-networks, policy-gradient methods, actor-critic algorithms, and multi-agent problems.

This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Cross-Listed: DSCI-503

This is an introductory course in machine learning intended primarily for students majoring or minoring in Mathematics, Data Science or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets and recommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks). Cross-Listed: DSCI-508

This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course covers introduction to robotics, applications of robots, return-on-investment, abstract models, controlling robot motion, complex motion, robotic sensors, input/output, external sensors, threads, event programming, remote communication, remote sensing, behavior programming, and human/robot interfaces. Students will gain hands-on experience with emerging robot technologies, understand industrial applications of robots, and ramifications of human/robot interaction.

This course provides students with an introduction to the theory and practice of computer vision - the practice of analyzing visual images from the world. This course will cover a wide range of computer techniques and problems that address "how we make computers see".

This one semester course is designed to introduce the students to the fundamental concepts underlying the study of linear algebra. Topics include matrix algebra; systems of linear equations; vector spaces and subspaces; basis and dimensions; orthogonality; determinants; eigenvalues and eigenvectors; diagonalization of matrices; and linear transformations.