斯坦福大學(xué)計(jì)算機(jī)類科研

斯坦福大學(xué)計(jì)算機(jī)類科研

Stanford University CS/EE Research

科研時(shí)間：

6月1日--9月1日，每期時(shí)間長度為3—4周；

（針對假期只有3周的學(xué)生，可選3周實(shí)地+1周遠(yuǎn)程，確保科研收獲）

科研主題：

博弈論、人工智能、機(jī)器學(xué)習(xí)、機(jī)器人、計(jì)算機(jī)、大數(shù)據(jù)及應(yīng)用、AlphaGo

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欲以申請美國常青藤名校計(jì)算機(jī)、電子工程、機(jī)械、統(tǒng)計(jì)、數(shù)學(xué)等相關(guān)專業(yè)的本科生、研究生為主

科研概述：

Reinforcement Learning (RL) is a research of methods that train an agent to maximize reward by interacting with the environment. By learning from experience and exploring the environment, the agent can learn about the dynamics of the environment and figure out best ways to accomplish tasks.

Deep Learning (DL) is a research of methods that take inspiration from signal processing in the brain. Through massively parallel computation with millions of neurons, the system can learn to accomplish complex tasks such as visual perception, audio understanding, natural language translation, and even reasoning.

In this project, we combine reinforcement learning and deep learning to train an agent that can interact with a complex environment. With deep learning, the agent can process complex visual input typically associated with a robotics or game environment, and with reinforcement learning, the agent can learn to accomplish goals based on its processing of the environment.

Example applications of combination of RL and DL include AlphaGo from Google Deepmind, and advanced robotics control from OpenAI and UC Berkeley.

Requirement

To accomplish the project, participants are expected to posses the following skills

Required:

1. Strong programming skills, familiar with at least one programming language.

2. Good math skills, familiar with algebra and probability.

Recommended:

1. Experience with using linux-based shell environments.

2. Basic knowledge of linear algebra.

3. Familiarity with python.

4. Basic knowledge of machine learning.

This project is advanced and challenging. If the student does not have enough prior experience to finish the project, he or she may participate in a simplied version of the project. i.e. reinforcement learning only or deep learning only, depending on the specific situation of the student.

科研亮點(diǎn)：

1. 進(jìn)入美國名校實(shí)驗(yàn)室/科研組，接觸尖端科學(xué)

為未來赴美深造做準(zhǔn)備；科研經(jīng)歷是美國名校申請的基石，頂級名校的科研項(xiàng)目是對學(xué)生有能力完成名校學(xué)業(yè)最好的證明。

2. 師從導(dǎo)師開展實(shí)驗(yàn)項(xiàng)目

高層次的人脈和校友關(guān)系，與學(xué)生為伍的人是諾貝爾獎(jiǎng)獲得者、美國科學(xué)院院士、教授、名校博士、碩士，學(xué)生將體驗(yàn)到世界最頂級學(xué)術(shù)專家們的思想和氣質(zhì)。

3. 獲得導(dǎo)師推薦信和科研證書

對于優(yōu)秀學(xué)生可以獲得名校導(dǎo)師的推薦信，大大助力未來的留學(xué)申請；

4. 全天候?qū)I(yè)英語環(huán)境，迅速提升專業(yè)水平

提升溝通和專業(yè)英語水平，提升專業(yè)知識和能力，用實(shí)踐使學(xué)生的理論知識更加具體形象。

5. 高含金量收獲助力未來留學(xué)深造及就業(yè)

在名校導(dǎo)師指導(dǎo)下的科研過程將幫助學(xué)生明確自身發(fā)展方向，不斷深化對于美國學(xué)界的了解與認(rèn)同，幫助參與學(xué)生及家長明確未來的學(xué)校及專業(yè)申請方向；從而更好的明確留學(xué)的目的與意義，擺脫盲目，獲得真知。

科研收獲：

1.科研完成時(shí)，學(xué)生將會全面了解生命科學(xué)類基本知識和最新進(jìn)展。

2.挑戰(zhàn)自身潛能，切身體會斯坦福大學(xué)頂尖科研環(huán)境，在嚴(yán)苛的訓(xùn)練下快速成長。

3.極大拓寬視野，實(shí)地感受國內(nèi)外科研區(qū)別。通過此次科研，參與學(xué)生將會對留學(xué)名校有個(gè)清晰的認(rèn)識，并依此做出最優(yōu)的人生規(guī)劃。

4.學(xué)生將有機(jī)會與頂尖教授零距離交流套磁，了解斯坦福的內(nèi)部申請信息。

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