DATA 2810: Introduction to Artificial Intelligence
This 3-credit hour course traces the evolution of AI from the perceptron to modern day large language models. Students will be exposed to the major branches of AI and the key architectures that allow computers to mimic (limited) human intelligence. Along the way, students will understand the strengths and weaknesses, and different capabilities, of different AI architectures. Students will be able to speak intelligently about the current types of AI, and they will be able to identify constructive use cases, as well as potential risks and dangers of AI.
Course Goals
- Differentiate between machine learning and other types of AI.
- Explain how AI systems can become biased, the consequences of this bias, and how these can be reduced or eliminated.
- Define and describe common concepts related to AI, such as weak and strong AI, artificial general intelligence, supervised and unsupervised learning, classification and regression, and discriminative and generative AI.
- Define and describe fundamental structures related to AI, such as algorithms, models, neural networks.
- Describe generally the operations and structure of different types of neural networks and how tat structure helps them perform specific tasks involving images or language.
Section(s) are taught by CAIDS Professor of Practice, John Levendis.
Prerequisites
DATA 1010: Introduction to Data or Instructor Permission
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