Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This first course of the two would focus more on mathematical tools, and the other course would focus more on algorithmic tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重數學類的工具，而另一課程將較為著重方法類的工具。]
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
Instructor: 林軒田
Access provided by Abu Dhabi National Oil Company
49,579 already enrolled
Gain insight into a topic and learn the fundamentals.
937 reviews
Beginner level
No prior experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
97%
Most learners liked this course
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
2 assignments
Taught in Chinese (Traditional)
See how employees at top companies are mastering in-demand skills
There are 8 modules in this course
what machine learning is and its connection to applications and other fields
What's included
5 videos5 readings
your first learning algorithm (and the world's first!) that "draws the line" between yes and no by adaptively searching for a good line based on data
What's included
4 videos
learning comes with many possibilities in different applications, with our focus being binary classification or regression from a batch of supervised data with concrete features
What's included
4 videos
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
What's included
4 videos1 assignment
what we pay in choosing hypotheses during training: the growth function for representing effective number of choices
What's included
4 videos
test error can approximate training error if there is enough data and growth function does not grow too fast
What's included
4 videos
learning happens if there is finite model complexity (called VC dimension), enough data, and low training error
What's included
4 videos
learning can still happen within a noisy environment and different error measures
What's included
4 videos1 assignment
Instructor
Instructor ratings
(71 ratings)Offered by
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
- 5 stars
92.42%
- 4 stars
6.18%
- 3 stars
0.64%
- 2 stars
0.42%
- 1 star
0.32%
Showing 3 of 937
IW
Reviewed on Nov 18, 2017
Thank Prof. Lin and coursera for providing me with the platform and courses to make me better. Last but not least, the quiz is quite difficult...
FF
Reviewed on Apr 19, 2018
Best course for the learners with some concepts of machine learning
ZL
Reviewed on Aug 26, 2020
林老师这部分课程内容偏理论，对数理基础有一定的要求。每次作业题需要认真思考，作业后面的编程部分也能锻炼实践能力，进一步巩固所学习的理论知识。总体来说质量不错，不过希望老师以后能以后对课件里面的问题进行总结的时候可以不用太story-like，或者说更简练一点，我认为这样可以有助于学习暂停视频并好好理解。