ECE 1410

How Machines Learn: AI from the Perceptron to GPT

Course Description

Course information provided by the 2025-2026 Catalog.

This course covers learning, deep learning, and neural networks from the perceptron through modern architectures such as GPT. Students will build intuition for how machines learn, explore foundational neural architectures, implement learning algorithms on neural networks, learn about small-scale and large-scale hardware architectures for leaning and AI, efficiency of and energy cost of AI systems, and reflect on the ethical and societal implications of AI. More advanced topics such as recurrent neural networks (RNNs), long short-term memory (LSTMs), Transformers and Large Language Models will be introduced at a high level with emphasis on intuition and demonstrations rather than mathematical details. The course emphasizes concepts and applications with mathematical tools that freshmen can wield to connect engineering tools with AI methods. The course also discusses AI hardware topics related to power consumption, computational infrastructure requirements, and the role of large-scale data centers in enabling AI systems. Some programming experience in Python (or an equivalent programming language) will be useful in completing assignments and design projects.

Last 4 Terms Offered (None)

Learning Outcomes

• Analyze multi-layer perceptron neural networks.
• Implement learning algorithms (e.g. gradient descent) on neural networks.
• Analyze hardware costs in terms of energy and infrastructure for deep learning neural networks.
• Demonstrate understanding of Transformers and Large Language Models.
• Demonstrate teamwork in pursuing design challenges and goals.
• ABET 1: Demonstrate ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
• ABET 2: An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
• ABET 3: An ability to communicate effectively with a range of audiences.
• ABET 4 An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
• ABET 7: An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

View Enrollment Information