CS2402Introduction to Computational Probability Modeling

这门课学什么

课程简介

Due to the inherent uncertainty in the world, probability and statistics are used in many areas of computer science, such as data science, artificial intelligence, bioinformatics, networking, algorithms, and software testing. In this course, students will learn concepts for computational modeling of random phenomenon, probability, and statistical inference. Students will write computer programs to simulate random phenomenon and analyze real-world data with computational probability models.

校方 PDF 第 2 页 · Abstract

课程内容

• Introduction to Python • variables, data types, operators • conditional statements, loops • functions, classes • scientific computing packages: Jupyter, scipy, numpy, matplotlib • Simulating random phenomenon • Frequentist probability, laws of probability • Games of chance, expected gain, expected utility • Failure rates and redundancy, birthday problem • Arrival time, Brownian motion • Measuring random phenomenon • Estimating parameters, confidence intervals • Hypothesis testing • Inference with Bayes Rule • Conditional probability, Bayes’ rule • Text analysis with Naïve Bayes classifiers • Decision making with subjective probability and optimal Bayes search • Prediction with logistic regression • Time-series modeling with Markov models

校方 PDF 第 6–7 页 · Keyword Syllabus

预期学习成果

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CILOs                                              Weighting (if DEC-A1          DEC-A2          DEC-A3
                                                                  app.)
1              Describe and explain how computational                                           x
               probability models are used in computer
               science, information technology, and data
               science.
2              Design critical thinking to use computational                                    x
               probability models to explain random
               phenomenon through independent
               investigation.
3              Write computer programs to simulate                                              x
               random phenomenon, and analyze data with
               computational probability models.

校方 PDF 第 2 页 · Course Intended Learning Outcomes (CILOs)

我能不能修这门课

先修、建议先备和并修要求分别查看；等同及互斥条件也会影响选课。and 表示同时满足，or 表示备选；组合条件按下方原文分组阅读。

先修条件

CS1102 Introduction to Computer Studies or CS1103B Media Computing or CS1302 Introduction to Computer Programming or CS1302A Introduction to Computer Programming or SM1103A Introduction to Media Computing

校方 PDF 第 1 页 · Prerequisites

建议先备知识

无（校方注明 Nil）

校方 PDF 第 1 页 · Precursors

并修要求

该版官方课纲未列明该项。

其他修读要求

该版官方课纲未列明该项。

等同课程及条件

GE2326 Probability in Action: From the Unfinished Game to the Modern World

校方 PDF 第 1 页 · Equivalent Courses

互斥课程及条件

无（校方注明 Nil）

校方 PDF 第 1 页 · Exclusive Courses

怎么考，怎样才算通过

考核方式

Continuous Assessment (%): 50; Examination (%): 50; Examination Duration (Hours): 2

查看校方原始考核表与使用要求

ATs                   CILO No.              Weighting (%)    Remarks              Allow Use of
                                                                                                        GenAI?
1                     Class participation 1, 2                    10               -                    Yes
2                     Assignments           2, 3                  20               3 individual         No
                                                                                   assignments
3                     Midterm               1, 2                  20               1 midterm            No

校方 PDF 第 3–4 页 · Continuous Assessment (%),Examination (%),Examination Duration (Hours)

通过条件

Minimum Examination Passing Requirement (%): 30 Additional Information for ATs: For a student to pass the course, at least 30% of the maximum mark for the examination must be obtained.

校方 PDF 第 4 页 · Minimum Examination Passing Requirement (%),Additional Information for ATs