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# AI网课代修 computer science代写

2022-03-30 12:07 星期三 所属： course代写 浏览：133

## 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 thisclass

• 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

Your final grade will be determined via the following percentages: Lecture participation points: 10%

Homework:45%

Final:45%