Applied Machine Learning

Course #CS3332

Est.imated Completion Time: 3 months

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.

Offerings database access

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Application Deadlines

No upcoming deadlines.