Courses

Course Prerequisites

• ENGR 102 Engineering Lab I – Computation
• MATH 251 Engineering Mathematics III

Course Description

• Engineering application-focused introduction to Machine Learning covering key machine learning concepts, guidance on selecting machine learning models, and application of python-based tools for data preparation, model development, and performance evaluation. Practical engineering use-cases for machine learning from electronics, energy, motors, robotics, security, computer systems, and health. Machine learning laboratory projects including dataset management, ML model development, visualization, and practical application showcasing ML expertise.

Course Objectives

• Identify the basic techniques used in learning systems in real-world engineering applications. Skills gained through text, lecture, labs, and final project.
• Format, manipulate, and interact with engineering datasets in Python
• Apply dataframe management methods to overcome missing, non-numerical, and grossly incorrect values in a dataset
• Characterize data with common statistical techniques.
• Identify appropriate learning techniques for representative engineering applications.
• Apply regression techniques for engineering problems.
• Model and solve engineering classification problems with several classification techniques.
• Develop and train neural networks for image detection and signal analysis problems.
• Identify important limitations in the application of machine learning, including ethics issues, through class discussion and case-study evaluations.

Course Prerequisites

• Graduate standing
• Recommended: Familiarity with programming language Python and basic knowledge of multivariate calculus, statistical inference, and linear algebra

Course Description

• A broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, regression, clustering, and classification; with applications to scientific and online data. Cross Listings: CSCE 676 and STAT 639

Course Objectives

• Acquire knowledge of foundations and application of methods in data mining and data analysis; prepare students to use methods and tools of data science in research in methods or applications.
• Conduct exploratory data analysis including visualization and summarization
• Apply selected unsupervised machine learning methods to data analytical problems
• Apply selected supervised machine learning methods to data analytical problems
• Select appropriate machine learning method applicable to common problem types
• Understand usage rationale, underpinnings, and limitations of machine learning methods
• Apply common libraries and tools to data analytical problems

• Course Prerequisites
  • MATH 251 or 253 and ECEN 248
• Course Description
  • This course will introduce the student to the fundamental concepts of probability theory applied to engineering problems. Its goal is to develop the ability to construct and exploit probabilistic models in a manner that combines intuition and mathematical precision. The proposed treatment of probability includes elementary set operations, sample spaces and probability laws, conditional probability, independence, and notions of combinatorics. A discussion of discrete and continuous random variables, common distributions, functions, and expectations forms an important part of this course. Transform methods, limit theorems, modes of convergence, and bounding techniques are also covered. In particular, special consideration will be given to the law of large numbers. Examples from engineering, science, and statistics will be provided.
• Course Objectives
  • Review basic notions of set theory and simple operations such as unions, intersections, differences and De Morgan’s laws. Discuss Cartesian products and simple combinatorics. Go over the counting principle, permutations, combinations and partitions.
  • Understand mathematical descriptions of random variables including probability mass functions, cumulative distribution functions and probability density functions. Become familiar with commonly encountered random variables, in particular the Gaussian random variable.
  • Engage the student in active learning through programming challenges, problem solving, and real-world examples. Encourage the student to become an independent learner and increase their awareness of available resources.