Course Syllabus: Introduction to AI – Probabilistic Reasoning and Decision Making
代修人工智能网课 (Programming assignments are completed in the language of the student’s choice.) Students of all backgrounds are welcome.
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 learning in Bayesian networks
1. Critical information, at a glance 代修人工智能网课
- 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.
- You should score at least 60% in the final exam to get a passing grade for this class, regardless of your overall percentage.
- We will have 3 homework assignments over the 4 week period.
2. Pre-requisites 代修人工智能网课
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.
(Programming assignments are completed in the language of the student’s choice.) Students of all backgrounds are welcome.
3. What you will learn from this class
- Describe and use different probabilistic models including Bayes Nets and EM algorithm
- Apply probabilistic models to solve real-world problems
- Design specific models for AI tasks
- Perform inference using probabilistic models 代修人工智能网课
- Prove relationships between probabilities under different models
- Implement core algorithms of different models
- Describe how agents learn from data using maximum likelihood learning
- Identify ethical concerns related to AI