機器學習技法 (Machine Learning Techniques)

National Taiwan University

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Welcome! The instructor has decided to teach the course in Mandarin on Coursera, while the slides of the course will be in English to ease the technical illustrations. We hope that this choice can help introduce Machine Learning to more students in the Mandarin-speaking world. The English-written slides will not require advanced English ability to understand, though. If you can understand the following descriptions of this course, you can probably follow the slides. [歡迎大家!這門課將採用英文投影片配合華文的教學講解,我們希望能藉這次華文教學的機會,將機器學習介紹給更多華人世界的同學們。課程中使用的英文投影片不會使用到艱深的英文,如果你能了解以下兩段的課程簡介,你應該也可以了解課程所使用的英文投影片。]

In the prequel of this course, Machine Learning Foundations, we have illustrated the necessary fundamentals that give any student of machine learning a solid foundation to explore further techniques. While many new techniques are being designed every day, some techniques stood the test of time and became popular tools nowadays.

The course roughly corresponds to the second half-semester of the National Taiwan University course "Machine Learning." Based on five years of teaching this popular course successfully (including winning the most prestigious teaching award of National Taiwan University) and discussing with many other scholars actively, the instructor chooses to focus on three of those popular tools, namely embedding numerous features (kernel models, such as support vector machine), combining predictive features (aggregation models, such as adaptive boosting), and distilling hidden features (extraction models, such as deep learning).


Syllabus

Each of the following items correspond to approximately one hour of video lecture. [以下的每個小項目對應到約一小時的線上課程]

Embedding Numerous Features [嵌入大量的特徵]
-- Linear Support Vector Machine [線性支持向量機]
-- Dual Support Vector Machine [對偶支持向量機]
-- Kernel Support Vector Machine [核型支持向量機]
-- Soft-Margin Support Vector Machine [軟式支持向量機]
-- Kernel Logistic Regression [核型羅吉斯迴歸]
-- Support Vector Regression [支持向量迴歸]

Combining Predictive Features [融合預測性的特徵]
-- Bootstrap Aggregation [自助聚合法]
-- Adaptive Boosting [漸次提昇法]
-- Decision Tree [決策樹]
-- Random Forest [隨機森林]
-- Gradient Boosted Decision Tree [梯度提昇決策樹]

Distilling Hidden Features [萃取隱藏的特徵]
-- Neural Network [類神經網路]
-- Deep Learning [深度學習]
-- Radial Basis Function Network [逕向基函數網路]
-- Matrix Factorization [矩陣分解]

Summary [總結]

Recommended Background

The basic knowledge on Calculus (differentiation), Linear Algebra (vector and matrix operations) and Probability (independent and dependent events) will be helpful. Some homeworks will require writing simple code so some programming background (on any platform) is recommended. We assume that the students have taken the NTU-Coursera "Machine Learning Foundations" class or equivalent. [我們希望修課的同學對於基本的微分、向量與矩陣運算、及機率的工具有所了解。有些作業會需要寫作或執行一些程式,所以我們建議修課的同學能在你所熟悉的平台上有一些程式寫作的背景。我們假設修課的同學們已經學過「機器學習基石」或同等課程。]

Suggested Readings

The lectures are designed to be self-contained. For reading before the course starts, we recommend (but do not require) that students refer to the book Learning from Data, which contains most of the background materials for this course. [這門課的錄影課程及投影片應該足以幫大家了解所有的內容。有關開課前的預備知識,我們推薦有興趣的同學們閱讀 Learning from Data 一書,該書包含了本課程所需的大部份背景知識。]

Course Format

The class will consist of lecture videos that contain integrated quiz questions. There will also be bi-weekly homeworks that are not part of video lectures. [這門課主要以線上錄影課程及其中的小測驗組成,每兩週我們會有另外的作業練習。]

FAQ

  • Will I get a statement of accomplishment after completing this class? [我在完成課程後,是否能得到「修業合格證明」?]

    Yes. Students who pass the basic course requirements will receive a statement of accomplishment signed by the instructor. [是的,當同學成功地達成課程的基本要求後,即可收到由授課老師簽署的「修業合格證明」。]

  • What equipment/resource do I need for taking this class? [修習此課需要哪些設備/資源?]

    You need access to some computing platform to run the code for some of the homework problems, and you can basically use any programming language of your choice. Each homework set should take no more than a whole day of your "machine time" on a modern PC if the algorithms are properly implemented. So you do not need a super-fast computing resource. [在有些作業的問題中,你需要在某些計算平台上執行程式,而你可以使用任何你所愛的程式語言。如果你正確的撰寫演算法,在一般的個人電腦上,每次作業所需的「機器時間」應該不到一天。所以你不需要超快的運算資源。]

  • What can I learn from this course? [我在此課程可以有什麼收穫?]

    A solid understanding of the most popular tools in machine learning! [對機器學習中最熱門的工具有堅固的了解!]

Dates:
  • 10 November 2015, 8 weeks
  • 23 December 2014, 8 weeks
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: Chinese Cn

Reviews

No reviews yet. Want to be the first?

Register to leave a review

Show?id=n3eliycplgk&bids=695438
Included in selections:
Small-icon.hover Deep Learning
Good materials on deep learning.
NVIDIA
More on this topic:
Logo 機器學習基石 (Machine Learning Foundations)
Machine learning is the study...
Cover 计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation
讲授计算机辅助翻译技术的基本概念,及多种辅助翻译工...
6faca5d4-40b8-4ee1-b775-96fa807719ed-da7e8d422f14.small 大数据机器学习|Big Data Machine Learning
《大数据机器学习》课程是面向信息学科的高年级本科生...
Logo 機器學習技法 (Machine Learning Techniques)
The course extends the fundamental...
More from 'Computer Science':
Ruby-primer Ruby 初学者
今天就开始学Ruby吧!你学Ruby是为了好玩,为...
Dsc_0856__ Introduction to Computing 计算概论A
计算概论A是针对“信息科学技术学科一年级本科生”...
Sjjg_608x211_info 数据结构与算法 Data Structures and Algorithms
“数据结构与算法”是计算机学科中的核心基础课程。课...
-2013-09-01-05.47.51 人群与网络 People & Networks
学习运用计算思维分析社会学、经济学问题的方法,加深...
More from 'Coursera':
A 中國古代歷史與人物--秦始皇
本課程是作為歷史入門通識而設計,重點在於藉由歷史教...
Humanities 中國人文經典導讀 | Classics of Chinese Humanities: Guided Readings
本課程對象為對中國文化有興趣的同學。 課程目的為通...
Version-a_2 The Beauty of Kunqu Opera | 崑曲之美
本課程以崑曲的歷史文化、文學、音樂、表演、美學為核...
Geometrical-optics 基礎光學 I (Introduction to Optics I)
以深入淺出的概念說明日常生活與科學應用中常見的光學...
Istock_000000248160_small 機率 (Probability)
這是一個機率的入門課程,著重的是教授機率基本概念。...

© 2013-2019