CS2402Introduction to Computational Probability Modeling

What you will study

Course overview

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.

Official PDF, page 2 · Abstract

Course content

• 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

Official PDF, pages 6–7 · Keyword Syllabus

Intended learning outcomes

View official learning outcomes and conditions

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.

Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)

Before you take this course

Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.

Prerequisites

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

Official PDF, page 1 · Prerequisites

Precursors

Nil

Official PDF, page 1 · Precursors

Corequisites

This edition of the official syllabus does not state this item.

Additional course requirements

This edition of the official syllabus does not state this item.

Equivalent courses and conditions

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

Official PDF, page 1 · Equivalent Courses

Exclusive courses and conditions

Nil

Official PDF, page 1 · Exclusive Courses

Assessment and passing the course

Assessment

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

View the original assessment table and instructions

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

Official PDF, pages 3–4 · Continuous Assessment (%),Examination (%),Examination Duration (Hours)

Passing requirements

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.

Official PDF, page 4 · Minimum Examination Passing Requirement (%),Additional Information for ATs