Introduction to Supervised Machine Learning in partnership with Ladies Who Tech & R-Ladies

Beijing
Thu, Jul 9, 2020, 7:30 PM (CST)

About this event

Ladies Who Tech携手R-Ladies: 机器学习· 监督式学习速成班
Ladies Who Tech x R-Ladies: Introduction to Supervised Machine Learning

机器学习是当前技术领域最热门的话题之一。 据Indeed.com称,仅在过去三年中,机器学习工作增加了344%。

Machine learning is one of the hottest sectors in tech at the moment. In the past three years alone, machine learning jobs increased by 344 per cent, according to Indeed.com.

机器学习是一类利用当前数据训练模型和算法的人工智能,它是一种随着您使用的越多而变得越“聪明”的技术。 机器学习还分为监督学习和无监督学习。

A class of artificial intelligence that takes current data to train models and algorithms, machine learning is a technology that gets “smarter” the more you use it. It also comes in two forms: supervised and unsupervised.

监督式学习要求操作员设置所需的输出,标记数据并为其提供参数。机器需要有明确的目标,很清楚自己想要什么结果,因此非常适合新接触机器学习的公司。

Supervised machine learning requires the operator to set the desired output, label the data and give it parameters. It ensures the model doesn’t make mistakes and is heading in the right direction, which makes it ideal for companies that haven’t deployed machine learning before.

继上次活动的热烈反响,Ladies Who Tech将会于7月9日(星期四)再与R-Ladies北京携手举办一项免费线上工作坊 ---《机器学习· 监督式学习速成班》。我们很荣幸能够再次邀请到资深学者和行为经济学,人工智能和社会数据科学研究员柯玫瑰(Hannah Rose Kirk)来教授大家监督式学习的基础知识和算法,以及探索如何用模型参数来提高机器学习模型的预测精度。

Back by popular demand, Ladies Who Tech and R-Ladies are co-hosting another free online workshop on “Introduction to Supervised Machine Learning” on 9th July, Thursday. We are proud to have Hannah Rose Kirk, an expert and long-time Beijing based researcher on behavioural economics, AI and social data science to be our host again for the session. She will lay the foundations of supervised machine learning, explain what algorithms and approaches we can use and how we parameterize models to fine-tune our predictions.

什么是监督式学习?
What is supervised machine learning and why is it important?

如上所述,监督式学习算法是根据在现有示例上构建的模型来预测未来或未知结果。

As mentioned above, supervised machine learning algorithms predict future or unknown outcomes reasoned from models built on existing examples.

该过程是“受监督的”,因为该算法会从训练数据集中学习,类似于教师如何监督其学生的学习过程。

The process is “supervised” because how the algorithm learns from the training dataset is analogous to how a teacher supervises the learning process of their student.

监督式学习可以用来解决回归问题和分类任务,它的算法会尝试建立起输入特征与目标结果之间的联系和依赖关系,因而通过使用一个已有数据集的训练过程,就可以预测出一个新的数据集所对应的输出结果。

Supervised machine learning can be used to attack regression problems and classification tasks, where complicated features can be distilled into predictions whether an outcome will occur or not.

监督式学习可以帮助回答许多领域的问题,例如:

• 医学:乳腺癌肿瘤是恶性的吗? 患者有患糖尿病的风险吗?

• 通讯:这是垃圾邮件吗? 这讯息是假新闻吗?

• 自动驾驶汽车:此图像是人行横道还是停车标志?

• 财务:此借款人会偿还还是拖欠其贷款?

Supervised machine learning is versatile and the problems it can help to answer span many fields such as:

• Medicine: Is a breast cancer tumor malignant? Am I at risk of diabetes?

• Communications: Is this email spam? Is this message fake news?

• Self-Driving cars: Is this image a pedestrian crossing or a stop sign?

• Finance: Will this borrower repay or default on their loan? 

速成班内容
What will you learn

Hannah将使用两个数据集,一组肿瘤中的乳腺癌检测和另一组孕妇的糖尿病诊断,来解释如何使用四种类型的机器学习模型:K-近邻算法,逻辑回归,决策树和随机森林。

Using two datasets, one for breast cancer detection in tumors and another for diabetes diagnosis in pregnant women, Hannah will explain how to use four types of machine learning models: K-Nearest Neighbors, Logistic Regression, Decision Trees and Random Forests.

这些模型的多功能性和灵活性使其成为功能强大的预测工具,可以简单适应您的专业或学术环境。而我们遇到的许多问题都可以描述为分类任务,本教程将帮助您利用机器学习的力量来理解您的数据并洞悉手头的任务。

The versatility, flexibility and surprising simplicity of these approaches make powerful predictive tools which you can adapt to your own professional or academic environment. Many problems can be described as classification tasks where an outcome either happens or it doesn’t. This tutorial will help you harness the power of machine learning to understand your data and get insight on the task at hand.

本教程将包含监督式机器学习及其方法的描述性概述,以及将这些技术付诸实践的动手编程示例。

The tutorial will contain a descriptive overview of supervised machine learning and its methods, alongside hands-on coding examples for putting these techniques into practice.

该活动面向所有级别的R用户。初学者将对监督式学习的工作原理有一个入门性的了解,而更高级的编码人员将受益于全面的实用数据分析工具包。

Any level experience of R is welcome. Complete beginners will gain an introductory understanding to how supervised machine learning works and more advanced coders will benefit from a comprehensive practical toolkit for data analytics.

无论您是机器学习领域的新手,还是想提高自己的知识水平,都不要错过这次机会!

Whether you’re new to the machine learning field or looking to advance your knowledge, this is your moment!

关于R-Ladies
About R-Ladies

R-Ladies是一个由R用户或R初学者组成的全球社区,致力于使R社区更加具有性别包容性。R-Ladies通过举办培训,讲习班和社交活动,以帮助参会人彼此了解并谈论Ta们作为数据科学女性或R用户和学习者的经历。

R-Ladies is a global community of R users or beginners who are committed to making the R community more gender-inclusive. R-Ladies will generally host tutorials, workshops, and social events to help attendees get to know each other and talk about their experiences as women in data science or as R users and learners.

Past Speakers

  • Hannah Rose Kirk

    Hannah Rose Kirk

    Peking University

    Yenching Scholar

    See Bio

  • Brought to you by

    • Yasemin Cilt (Startup Grind)

      Yasemin Cilt (Startup Grind)

      Startup Grind

      Director

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    • Madeleine Madeleine

      Madeleine Madeleine

      Startup Grind Beijing | Startup Grind Greater China | Influence Matters

      Chapter Co-director

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    • Sabrina Wang

      Chapter Co-Director

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    • Effy Ai

      Effy Ai

      Beijing Foreign Studies University

      Marketing Team Lead

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    • Valentine Salim

      Valentine Salim

      Design & Marketing Core Team

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    • Rebecca Xie

      Rebecca Xie

      Plug and Play China

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