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Fall 2023
Jul 19, 2024
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Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.

CS 57100 - Artificial Intelligence
Credit Hours: 3.00.  Artificial Intelligence (AI) systems are increasingly being deployed in many real-world tasks. This course provides an introduction to the fundamental principles and applications of AI. The course covers classic material including search-based methods, probabilistic reasoning, game playing, decision making, exact and approximate inference, causal learning, and reinforcement learning as well as selected advanced topics. The focus of the course is on foundational methods and current techniques for building AI systems that exhibit 'intelligent' behavior and can 'learn' from experience. The course assumes students are familiar with basic concepts in analysis, linear algebra, optimization, discrete mathematics, elementary probability, statistics, data structures, and algorithms. Students are expected to have good programming and software development skills and have a working knowledge of Python and Java.
3.000 Credit hours

Syllabus Available
Levels: Undergraduate, Graduate, Professional
Schedule Types: Distance Learning, Lecture

Offered By: College of Science
Department: Computer Science

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Assess and explain the applicability, strengths, and weaknesses of the basic knowledge representation, problem-solving, and learning methods in solving a particular problem. (E,W) 2. Predict the behavior and estimate the cost (in time and space) of different heuristic and optimal search methods, and choose the appropriate method for particular problems. (W,P) 3. Develop small logic-based, rule-based, and search-based systems; be able to predict performance characteristics. (P) 4. Predict the behavior of basic machine-learning methods, and choose the appropriate method for particular problems. (W,P) 5. Communicate critical key issues in AI-related to knowledge representation, problem-solving, and learning for a specific problem. (W) 6. Propose, evaluate, and implement effective solutions to problems requiring AI techniques. (P) 7. Articulate key problems, both technical and philosophical, in the development of artificial intelligence systems. (E,W)



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