Description
The goal of this course is to provide background in the field of AI. The successful student will finish the course with specific modeling and analytical skills (e.g., search, logic, probability), knowledge of many of the most important knowledge representation, reasoning, and machine learning schemes, and a general understanding of AI principles and practices. The course will serve as a useful foundation to prepare the student for further study of graduate- level advanced AI courses such as machine learning, knowledge-based AI, game AI, natural language processing (NLP), image processing, etc.
This course will provide an introduction to the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics to be covered include the following:
• Introduction of AI: background, agents, environments, etc.
• Problem solving by search: principles of search, uninformed (blind) search, in- formed (heuristic) search, local search, constraint satisfaction, adversarial search (games), etc.
• Knowledge representation: knowledge bases and inference, theorem proving, propositional logic, first-order logic, inference, resolution, etc.
• Probability theory: probabilistic reasoning, representing uncertainty, Bayesian net- works, etc.
• Advanced/interesting topics: Machine learning, natural language processing (NLP), image processing, etc.
This course will provide an introduction to the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics to be covered include the following:
• Introduction of AI: background, agents, environments, etc.
• Problem solving by search: principles of search, uninformed (blind) search, in- formed (heuristic) search, local search, constraint satisfaction, adversarial search (games), etc.
• Knowledge representation: knowledge bases and inference, theorem proving, propositional logic, first-order logic, inference, resolution, etc.
• Probability theory: probabilistic reasoning, representing uncertainty, Bayesian net- works, etc.
• Advanced/interesting topics: Machine learning, natural language processing (NLP), image processing, etc.
General Information
Course Information
Long Title: Introduction to Artificial Intelligence
Lecture Times: Mon, Wed, Fri @ 11:30 AM - 12:20 PM
Location: DeBartolo Hall 140
Office Location: 211D Cushing Hall;
Office Hours: Mon, Wed, Fri @ 1:00 PM - 2:00 PM
Teaching Assistant (TA): Jian Yang (jyang9@nd.edu)
TA Office Hours: Tue, Thu @ 1:00 PM - 3:00 PM
TA Office Location: CSE TA office, 212 Cushing Hall
TA Contact Info: Office: 222 Cushing Hall. Phone: (574) 250 5399
Lecture Times: Mon, Wed, Fri @ 11:30 AM - 12:20 PM
Location: DeBartolo Hall 140
Office Location: 211D Cushing Hall;
Office Hours: Mon, Wed, Fri @ 1:00 PM - 2:00 PM
Teaching Assistant (TA): Jian Yang (jyang9@nd.edu)
TA Office Hours: Tue, Thu @ 1:00 PM - 3:00 PM
TA Office Location: CSE TA office, 212 Cushing Hall
TA Contact Info: Office: 222 Cushing Hall. Phone: (574) 250 5399
Name | Office Hours | |
---|---|---|
Jian Yang | When? Where? |
Supplementary Materials
Supplementary Materials
Date