English中文
将人工智能用起来
2019年6月18-21日
北京,中国
 
多功能厅6A+B (Function Room 6A+B)
多功能厅5A+B(Function Room 5A+B)
多功能厅2(Function Room 2)
紫金大厅B(Grand Hall B)
Add A practical guide toward explainability and bias evaluation in machine learning to your personal schedule
09:00 A practical guide toward explainability and bias evaluation in machine learning Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Add Design thinking for AI to your personal schedule
13:30 Design thinking for AI Chris Butler (IPsoft)
报告厅(Auditorium)
Add 基于深度学习的时间序列预测 (Deep learning for time series forecasting) to your personal schedule
09:00 基于深度学习的时间序列预测 (Deep learning for time series forecasting) Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft), Henry Zeng (Microsoft)
多功能厅8A+B(Function Room 8A+B)
Add Building reinforcement learning models and AI applications with Ray to your personal schedule
13:30 Building reinforcement learning models and AI applications with Ray Richard Liaw (UC Berkeley RISELab), Siyuan Zhuang (UC Berkeley RISELab)
多功能厅5C(Function Room 5C)
08:00 Morning Coffee | Room: 1st Floor Foyer
10:30 Morning Break | Room: 1st Floor Foyer
15:00 Afternoon Break | Room: 1st Floor Foyer
12:30 Lunch - sponsored by Intel AI | Room: 彩虹厅 (Rainbow Room)
09:00-17:00 (8h)
Deep learning with PyTorch (Day 2)
PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Rich Ott introduces you to the PyTorch workflow and explores how its easy-to-use API and seamless use of GPUs makes it a sought-after tool for deep learning. Join in to get the knowledge you need to build deep learning models using real-world datasets.
09:00-17:00 (8h)
Deep learning with TensorFlow (Day 2)
The TensorFlow library contains computational graphs with automatic parallelization across resources, which is ideal architecture for implementing neural networks. Season Yang introduces TensorFlow's capabilities in Python, and you'll then get your hands dirty building machine learning algorithms piece by piece while using the Keras API provided by TensorFlow with several hands-on applications.
09:00-17:00 (8h)
量化互联网金融信用与反欺诈风控 (Day 2)
您想了解金融企业是怎样利用大数据和人工智能技术来画像个人行为并检测欺诈用户的吗?互联网金融幕后的量化分析流程是怎么杨的?个人信用是怎样通过大数据被量化的?在实践过程中,机器学习算法的应用存在着哪些需要关注的方面?怎样通过图谱分析来融合多维数据,为我们区分正常用户和欺诈用户? 这套辅导课基于清华大学交叉信息研究院开设的一门"量化金融信用与风控分析”研究生课。其中会用LendingClub的真实借贷数据做为案例,解说一些具体模型的实现。
09:00-12:30 (3h 30m) 与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
A practical guide toward explainability and bias evaluation in machine learning
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Numerous high-profile incidents have proved undesired bias in machine learning a worrying topic. Alejandro Saucedo uses a hands-on example to demystify machine learning bias. You'll automate the loan-approval process for a company and explore key tools and techniques from the latest research that allows you to assess and mitigate undesired bias in machine learning models.
13:30-17:00 (3h 30m) 人工智能对商业及社会的影响 (Impact of AI on Business and Society), 英文讲话 (Presented in English)
Design thinking for AI
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler borrows from the principles of design thinking to lead you through exercises that help you create more impactful solutions and better align your team.
09:00-12:30 (3h 30m) 中英文讲话(Presented in Chinese and English)
基于深度学习的时间序列预测 (Deep learning for time series forecasting)
Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft), Henry Zeng (Microsoft)
Almost every business today uses forecasting to make better decisions and allocate its resources more effectively. Deep learning has achieved a lot of success in computer vision, text, and speech processing but has only recently been applied to time series forecasting. Join in to learn how and when to apply deep neural networks to time series forecasting. (presented in Chinese and English)
13:30-17:00 (3h 30m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
通过自动化机器学习民主化和加速AI落地 (Democratizing and accelerating AI through automated machine learning)
Henry Zeng (Microsoft), Lu Zhang (Microsoft), xiao zhang (Microsoft)
Intelligent experiences powered by AI seem like magic, but developing them is cumbersome, involving a series of time consuming sequential and interconnected decisions along the way. What if you had an automated service that could identify the best machine learning pipelines for your given problem or data? Lu Zhang, Henry Zeng, and Xiao Zhang detail how automated machine learning does that.
09:00-12:30 (3h 30m)
Intel OpenVINO:加速从边缘到云端的深度学习的推断和计算机视觉(Intel OpenVINO: Accelerating deep learning inference and computer vision from edge to cloud)
Zhen Zhao (Intel)
Intel OpenVINO provides a highly optimized cross-platform deep learning deployment and visual AI solution based on various Intel architectures. Join Zhen Zhao as she explains the structure and workflow of the Intel OpenVINO toolkit, optimization methods by asynchronies, heterogeneous computing, low-precision inference, and instruction set acceleration.
13:30-17:00 (3h 30m) 英文讲话 (Presented in English)
Building reinforcement learning models and AI applications with Ray
Richard Liaw (UC Berkeley RISELab), Siyuan Zhuang (UC Berkeley RISELab)
Ray is a general-purpose framework for programming your cluster. Richard Liaw leads a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.
09:00-12:30 (3h 30m) 实施人工智能 (Implementing AI)
Analytics Zoo:基于Apache Spark的分布式TensorFlow和Keras(Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark)
Zhichao Li (Intel), Kai Huang (Intel), Yang Wang (Intel)
Zhichao Li, Kai Huang, and Yang Wang show you how to build and productionize deep learning applications for big data using Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—illustrated though real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.
08:00-08:45 (45m)
Break: Morning Coffee
10:30-11:00 (30m)
Break: Morning Break
15:00-15:30 (30m)
Break: Afternoon Break
12:30-13:30 (1h)
Break: Lunch - sponsored by Intel AI