Applied Machine Learning

Course #CS3332

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Clock Est. completion in 3 months

Location pin Offered through Distance Learning

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Overview

Survey of machine-learning techniques of artificial intelligence with a particular focus on military applications.  Topics include types of machine learning, training and testing of machine learning, data preparation, decision trees, Bayesian reasoning, linear models, neural networks, case-based reasoning, and reinforcement learning.  Each method will be related to important military and government applications.  This course is intended for students who are not computer-science majors.

Included in Degrees & Certificates

  • 128
  • 367

Prerequisites

  • CS3331

Learning Outcomes

Define and recognize key machine-learning techniques and be able to explain them, including: Caching, case-based reasoning, and decision trees Concept learning of logical expressions Classification using probabilistic reasoning Neural networks, including convolutional and transformer-based Generative adversarial learning Heuristic search Evolutionary computing Ensemble learning Recommend appropriate machine-learning techniques for key applications including advisory systems, planning systems, natural-language understanding, computer vision, and sensor systems. Be able to clean and transform raw data to get it into a form efficiently usable by machine learning. Be able to apply software packages for key machine-learning techniques to real data and analyze their results. Be able to implement learning methods using a software tool. Identify the major difficulties in implementing and testing learning systems, including explaining reasoning, bias, and adversarial attacks.