CSCI-B 551 Elements of Artificial Intelligence
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
- Equivalent(s): CSCI 54900 Intelligent Systems
Description
Introduction to major issues and approaches in artificial intelligence. Principles of reactive, goal-based, and utility-based agents. Problem-solving and search. Knowledge representation and design of representational vocabularies. Inference and theorem proving, reasoning under uncertainty, and planning. Overview of machine learning.
Topics
Foundational concepts
- Philosophy, ethics, and safety of AI
Search and planning
- Solving problems by searching
- Search in complex environments
- Constraint satisfaction problems
- Adversarial search and games
- Automated planning
Logic and knowledge representation
- Logical agents
- Inference in first-order logic
- Knowledge representation
Probabilistic methods
- Probabilistic reasoning
- Probabilistic programming
- Learning probabilistic models
Learning
- Learning from examples
- Deep learning
- Reinforcement learning
- Deep learning for natural language processing
Applications and specialized domains
- Natural language processing
- Robotics
- Computer vision
- Multiagent decision making
Learning Outcomes
- Analyze various search algorithms and their trade-offs to solve complex problems in AI environments, applying critical thinking to select appropriate strategies. CS 1
- Design advanced logical agents that can make complex inferences and decisions using first-order logic, demonstrating proficiency in knowledge representation. CS 1
- Model uncertainty in dynamic environments by applying probabilistic reasoning techniques and evaluate the impact of different probabilistic models. CS 1
- Formulate and solve multi-agent decision-making scenarios by analyzing interactions among agents and predicting their strategic behavior, demonstrating strategic thinking skills. CS 1
- Develop deep neural networks for natural language processing tasks, implementing advanced techniques such as attention mechanisms and sequence-to-sequence models. CS 1
- Construct and evaluate robotic systems that involve perception, control, and planning, integrating knowledge from different AI domains to achieve desired outcomes. CS 4
- Create computer vision applications that involve complex image analysis and recognition tasks, demonstrating the ability to synthesize knowledge and apply advanced algorithms. CS 4
- Conduct research to explore emerging AI trends and technologies, demonstrating the ability to design experiments, gather and analyze data, and present findings coherently. CS 7
Policies and Procedures
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