ITI90046 学分已补充 Handbook

Mathematical foundations for data science and AI

莫纳什大学·Monash University·墨尔本

💪 压力

4 / 5

⭐ 含金量

5 / 5

✅ 通过率

打开官方页面 →返回课程列表

📖 课程概览

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.

📋 Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period 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. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

🧠 大神解析

### 📊 课程难度与压力分析 ITI9004（Mathematical foundations for data science and AI）是 Monash 信息技术方向的高频选课，学习压力通常来自概念框架、案例应用和结构化输出三者并行。前几周以框架搭建为主，中后段会叠加作业、项目、测验和期末复习，若没有固定节奏，容易出现“知道该怎么做但答不完整”的失分。建议从第一周建立稳定闭环：每周一次概念梳理、一次案例/项目实践、一次复盘纠错，把风险分散到平时处理。 ### 🎯 备考重点与高分策略高分关键是“依据充分 + 逻辑完整 + 结论可执行”。复习建议三轮推进：第一轮补齐核心概念与方法前提；第二轮按题型强化（案例分析题、论述题、计算/建模题、综合决策题），形成模板化答题结构；第三轮限时模拟，训练在压力下保持表达完整。答题建议先给结论框架，再展开证据链与步骤，最后说明边界条件、风险与替代方案。 ### 📚 学习建议与资源推荐资料优先级建议：课程讲义与 rubric > tutorial 案例与课堂讨论 > 历年题与补充资料。每周至少 45 分钟做错误归因，把问题归类为概念混淆、条件遗漏、计算疏漏、表达不清四类，并给每类配置一条下周可执行修正动作。建议维护“概念卡片 + 题型模板 + 错题索引”三件套，长期收益显著。 ### ⚠️ 作业与考试避坑指南常见扣分点包括：只写结论不写依据、忽略题干限定条件、案例信息引用不完整、结果缺乏解释。建议按 D-7 / D-3 / D-1 节奏推进：D-7 完成答案骨架，D-3 补齐证据链与边界条件，D-1 做格式核查与口径统一。 ### ✅ 执行建议每周固定 30 分钟复盘，记录“错误原因-修复动作-验证结果”，下周优先验证改进项。 ### 🧪 考前核对清单提交前逐项确认：是否明确结论、是否给出证据链、是否覆盖边界条件、是否解释风险与影响、是否完成表达润色。持续执行 8-10 周后，ITI9004 的稳定性与成绩通常会明显提升。

🎯 学习成果

Outcome 1

Explain fundamental concepts in calculus including basic differentiation and integration, and composite, inverse and parametric functions;

Outcome 2

Use trees and graphs to solve problems in computer science;

Outcome 3

Describe the principles of elementary probability theory, evaluate conditional probabilities and use Bayes' Theorem;

Outcome 4

Apply counting principles in combinatorics;

Outcome 5

Demonstrate basic knowledge and skills of linear algebra, including the manipulation of matrices, solution of linear systems, and evaluate and apply determinants;

Outcome 6

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.