PersonalUni
非官方 · 本页整理 Monash University Malaysia 的公开信息仅供参考。PersonalUni 与 Monash University Malaysia 无隶属关系,重要信息请以该校官方信息为准。
This page lists publicly available information about Monash University Malaysia for reference only. PersonalUni is unofficial and not affiliated with Monash University Malaysia.

蒙纳什大学马来西亚校区 / 课程

MAT9004

Mathematical foundations for data science and AI

6 credit pointsLevel 9First semester / Second semesterMalaysiaFaculty of Information Technology

基本信息

学分6 credit points
开课学期First semester / Second semester
校区Malaysia
考核构成
Continuous assessment40%
Assignment 130%
Mathematical challenge40%
Assignment 230%
Scheduled final assessment (3 hours and 10 minutes)60%

开课安排2

教学期授课方式状态
First semesterTeaching activities are on-campus (ON-CAMPUS)开课
Second semesterTeaching activities are on-campus (ON-CAMPUS)开课

以上为该校区在官方资料中登记的全部开课安排,不是汇总。同一门课可能在多个 教学期开课,也可能不同教学期的授课方式不同。

课程简介

Mathematical topics fundamental to computing and statistics including trees and other graphs, counting in combinatorics, principles of elementary probability theory, linear algebra, and fundamental concepts of calculus in one and several variables.

以上为 Monash Handbook 的官方原文,版权属 Monash University,此处按本站要求转载并标注出处: 官方页面 ↗

学习成果6

官方原文(Learning outcomes),版权属 Monash University。

  1. ULO1 Use trees and graphs to solve problems in computer science;
  2. ULO2 Apply counting principles in combinatorics;
  3. ULO3 Describe the principles of elementary probability theory, evaluate conditional probabilities and use Bayes' Theorem;
  4. ULO4 Demonstrate basic knowledge and skills of linear algebra, including the manipulation of matrices, solution of linear systems, and evaluate and apply determinants;
  5. ULO5 Explain fundamental concepts in calculus including basic differentiation and integration, and composite, inverse and parametric functions;
  6. ULO6 Perform key skills in the calculus of functions of several variables including the calculation of partial derivatives, find tangent planes and identify stationary points, root findings and convexity for optimisation.

教学方式与预期工作量

教学方式

Problem-based learning - On-campus and Monash Online

Peer assisted learning - On-campus

预期工作量

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. Scheduled activities may include a combination of teacher directed learning and online engagement.

官方原文,版权属 Monash University。

先修 / 同修要求

官方资料未列出该课程的先修要求。

修完这门课可以衔接

先修链路

官方资料未列出该课程的先修要求,因此没有链路可画。

互斥课程3

这些课和本课内容重叠,不能同时算进同一个学位(官方目录的 Prohibition 字段)。选了其中一门,另一门通常只能算选修学分甚至完全不计—— 这跟先修不同,先修是"没修过就不能选",互斥是"修了也不能两门都算"。

MAT1830Discrete mathematics for computer science
MAT1841该互斥课未在本站 Monash 数据内
ITI9004该互斥课未在本站 Monash 数据内

原始记号:MAT1830 AND MAT1841 AND ITI9004

属于这些学位2

这门课出现在下列学位的官方结构里。反过来说:如果你读的是这些学位之一,它大概率是要修的 (必修还是选修取决于它在 Part 里的位置,点进去看结构)。

数据来源

数据来源
官方网页
handbook.monash.edu
抓取时间
2026-09-13
可信度
程序抓取,未人工核实

查看官方完整描述 ↗ — 事实性字段(代码、学分、教学期、授课方式、考核权重、先修/同修/互斥关系)与 课程简介、学习成果、教学方式、预期工作量均取自官方 Handbook; 正文版权属 Monash University,此处转载并逐处标注出处。

发现信息有误?告诉我们。请用自己的话描述问题,不要上传成绩单、截图或校内系统文件

同级其他课程Level 9