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

Asset Publisher

Application Deadlines

08 Jul 2024

Fall Quarter applications due