FIT3154
Advanced data analysis
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | Second semester |
| 校区 | Malaysia |
| 考核构成 | Scheduled final assessment (2 hours and 10 minutes) — 60% Assignment 1 — 10% Assignment 2 — 10% Assignment 3 — 20% |
开课安排1 条
| 教学期 | 授课方式 | 状态 |
|---|---|---|
| Second semester | Teaching 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
This unit introduces the problem of machine learning and the major kinds of statistical learning used in data analysis. Learning and the different kinds of learning will be covered and their usage discussed. Evaluation techniques and typical application contexts will presented. A series of different models and algorithms will be presented in an exploratory way: looking at typical data, the basic models and algorithms and their use: linear and logistic regression, support vector machines, Bayesian networks, decision trees, random forests, k-means and clustering, neural-networks, deep learning, and others. Finally, two specialist topics will be covered briefly, statistical learning theory and working with big data.
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7 条
官方原文（Learning outcomes），版权属 Monash University。
- ULO1 Describe what machine learning is;
- ULO2 Differentiate kinds of statistical learning models and algorithms;
- ULO3 Evaluate a machine learning algorithm in typical contexts;
- ULO4 Describe and apply the major models and algorithms for statistical learning;
- ULO5 Identify the most competitive algorithms for typical contexts;
- ULO6 Compare and contrast the differences between big data applications and regular applications of algorithms;
- ULO7 Describe the theoretical limits of learning.
教学方式与预期工作量
教学方式
Active learning
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 teaching activities.
官方原文，版权属 Monash University。
先修 / 同修要求
以下先修关系按官方来源的结构化先修字段解析，原始记号：FIT2086
修完这门课可以衔接
先修链路
按官方先修字段的原始分组展开，AND / OR 的区别保留着—— 「A 或 B」和「A 与 B」在选课时是两回事。每门课点进去可以继续往下看。
属于这些学位1 个
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。