Course Syllabus: Introduction to AI
AI网课代修 Possible topics to be covered include probabilistic methods for reasoning and decision-making under uncertainty, inference and Bayesian networks
This class, Introduction to AI, is designed to be a junior-level computer science class that will introduce students to the probabilistic and statistical models at the heart of modern artificial intelligence. Possible topics to be covered include probabilistic methods for reasoning and decision-making under uncertainty, inference and Bayesian networks
1. Textbook
We will use the textbook Artificial Intelligence: Foundations of Computational Agents, 2nd ed. by Poole and Mackworth. An online version of this textbook can be found on the publisher’s website: https://artint.info/2e/html/ArtInt2e.html. A second reference book is Artificial Intelligence: A Modern Approach, 3rd ed by Russell and Norvig.
2. Pre-requisites AI网课代修
Prerequisites are elementary probability, statistics, linear algebra, and calculus, as well as basic programming ability in some high-level languages such as C, Java, Matlab, R, or Python. AI网课代修
Programming assignments are completed in the language of the student’s choice. Studentsshould satisfy the prerequisites before enrolling this class as AI classes are math heavy.
3. What you will learn from this class
- Describeand use different probabilistic models including Bayes Nets and EM algorithm
- Apply probabilistic models to solve real-worldproblemsC、Java、Matlab、R 或 PythonC、Java、Matlab、R 或 Python
- Design specific models for AItasks
- Perform inference using probabilisticmodels
- Proverelationships between probabilities under different models AI网课代修
- Implementcore algorithms of different models
- Describehow agents learn from data using maximum likelihood learning
- Identify ethical concernsrelated to AI
- Broad exposure to current AIdevelopments
4. Grading AI网课代修
Your final grade will be determined via the following percentages: Lecture participation points: 10%
Homework:45%
Final:45%
Important grading policies:
- Youmust score at least on the final exam to pass the If you score lower than 55 on the final, you will receive an F for the course, regardless of your overall average.
- Youmust score at least overall for the homework
- All homework should be done
- Accordingto the university’s policy, there is a threshold on the percentage of students who may receive A or A- in a class. The threshold for this class is . Please keep this policy in
5. Attendance Policy AI网课代修
Students are expected to participate in all lectures and missing more than 3 days of lectureswithout prior approval from the instructor will result in an F for the course regardless of lab orfinal exam scores. Based on the university’s policy, a student is considered late to class if he or she is late for more than 15 minutes for a class session.
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