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機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

Approx. 9 hours to complete

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Course Summary

This course covers algorithmic foundations, design and analysis of algorithms, data structures, and algorithmic techniques.

Key Learning Points

Learn about algorithmic foundations, design and analysis of algorithms, data structures, and algorithmic techniques.
Gain the skills needed to solve problems through algorithms and data structures.
Learn how to design algorithms and analyze their time and space complexity.

Learning Outcomes

Understand the fundamentals of algorithm design and analysis
Develop skills to design algorithms and analyze their time and space complexity
Gain knowledge in data structures and their applications in solving problems

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming concepts
Basic knowledge of mathematics

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Algorithms, Part I
Algorithms, Part II

Related Education Paths

Notable People in This Field

Thomas H. Cormen
Steven S. Skiena

Related Books

Description

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 second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical 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。本課程將較為著重方法類的工具，而另一課程將較為著重數學類的工具。]

Outline

第九講: Linear Regression
Linear Regression Problem
Linear Regression Algorithm
Generalization Issue
Linear Regression for Binary Classification
NTU MOOC 課程問題詢問與回報機制
課程大綱
課程形式及評分標準
延伸閱讀

第十講: Logistic Regression
Logistic Regression Problem
Logistic Regression Error
Gradient of Logistic Regression Error
Gradient Descent

第十一講: Linear Models for Classification
Linear Models for Binary Classification
Stochastic Gradient Descent
Multiclass via Logistic Regression
Multiclass via Binary Classification

第十二講: Nonlinear Transformation
Quadratic Hypothesis
Nonlinear Transform
Price of Nonlinear Transform
Structured Hypothesis Sets
作業三

第十三講: Hazard of Overfitting
What is Overfitting?
The Role of Noise and Data Size
Deterministic Noise
Dealing with Overfitting

第十四講: Regularization
Regularized Hypothesis Set
Weight Decay Regularization
Regularization and VC Theory
General Regularizers

第十五講: Validation
Model Selection Problem
Validation
Leave-One-Out Cross Validation
V-Fold Cross Validation

第十六講: Three Learning Principles
Occam's Razor
Sampling Bias
Data Snooping
Power of Three
作業四

Summary of User Reviews

Discover the NTU Machine Learning course on Coursera. This course teaches the algorithmic foundations of machine learning, including Exact Inference, Approximate Inference, and Learning Theory. Many users have praised the course for its comprehensive and clear explanations.

Key Aspect Users Liked About This Course

The course provides comprehensive and clear explanations.

Pros from User Reviews

Instructors are knowledgeable and engaging.
Assignments are challenging and rewarding.
Course content is relevant and up-to-date.
The community forum is helpful and supportive.

Cons from User Reviews

The course can be time-consuming and requires a lot of dedication.
Some of the mathematical concepts may be difficult to grasp for beginners.
The quizzes and exams can be difficult and require a lot of preparation.
The course is not suitable for those who are looking for a basic introduction to machine learning.

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機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. [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。]...

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