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
Offered By
National Taiwan University
About this Course
42,238 recent views
Learners taking this Course are
- Machine Learning Engineers
- Data Scientists
- Traders
- Data Analysts
- Research Assistants
Skills you will gain
Decision StumpPerceptronMachine LearningVc Dimension
Syllabus - What you will learn from this course
Week
12 hours to complete
第一講：The Learning Problem
what machine learning is and its connection to applications and other fields
5 videos (Total 70 min), 5 readings
5 videos
What is Machine Learning18m
Applications of Machine Learning18m
Components of Machine Learning11m
Machine Learning and Other Fields10m
5 readings
NTU MOOC 課程問題詢問與回報機制1m
課程大綱10m
課程形式及評分標準10m
延伸閱讀10m
homework 010m
Week
21 hour to complete
第二講：Learning to Answer Yes/No
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
4 videos (Total 61 min)
4 videos
Perceptron Learning Algorithm (PLA)19m
Guarantee of PLA12m
Non-Separable Data12m
Week
31 hour to complete
第三講：Types of Learning
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
4 videos (Total 61 min)
4 videos
Learning with Different Data Label18m
Learning with Different Protocol11m
Learning with Different Input Space14m
Week
42 hours to complete
第四講：Feasibility of Learning
learning can be "probably approximately correct" when given enough statistical data and finite number of hypotheses
4 videos (Total 60 min), 1 quiz
4 videos
Probability to the Rescue11m
Connection to Learning16m
Connection to Real Learning18m
1 practice exercise
作業一40m
4.9
124 Reviews29%
started a new career after completing these courses
43%
got a tangible career benefit from this course
36%
got a pay increase or promotion
Top reviews from 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
This course give a theoretical analysis of machine learning,though there is not much introduction of algorithm in detail,but this helped me open a new door of machine learning.
I am very grateful to the teacher for bring me to the world of Machine Learing. I am new in the field. I will try my best to learn the basic knowledge of ML.
Instructor
林軒田
教授 (Professor)資訊工程學系 (Department of Computer Science and Information Engineering)
About National Taiwan University
We firmly believe that open access to learning is a powerful socioeconomic equalizer. NTU is especially delighted to join other world-class universities on Coursera and to offer quality university courses to the Chinese-speaking population. We hope to transform the rich rewards of learning from a limited commodity to an experience available to all. More courses information, the official Facebook Page: https://www.facebook.com/ntumooc2017/
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.