FIT5215
Deep learning
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) — 40% Assignment 2 — 20% In class test 1 — 10% Assessment 1b — 20% In class test 2 — 10% Assessment 1a — 20% Assessment 2 — 20% Scheduled final assessment (2 hours and 10 minutes) — 40% Assignment 1 — 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
Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for you to develop practical knowledge of modern machine learning and deep learning and how they can be used in real-world settings such as image recognition or text clustering via neural embeddings. Learning activities will focus on designing machine learning systems, a broad landscape of supervised and unsupervised learning methods with a focus on modern deep learning knowledge for data analytics including deep neural networks, representation learning and embedding methods, and deep models used for time-series data which are rapidly used in science and industry.
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4 条
官方原文（Learning outcomes），版权属 Monash University。
- ULO1 Describe the life cycle of a machine leaning system, what is involved in designing such systems and strategy to maintain them;
- ULO2 Describe what deep learning (DL) is, access what makes DL work or fail and where they should be applied;
- ULO3 Develop and apply deep neural networks, convolutional neural networks, recurrent neural networks and different optimisation strategies for training them;
- ULO4 Develop unsupervised feature learning models and representation learning models.
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：((FIT9136 OR FIT9133) AND (MAT9004 OR EPM5026))
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。