诚心求比较EE@Columbia VS ECE@Brown

哥大：
全授课，30学分其中15必须选EE的（EECS也算） 其中有一些偏软的课程如下 （其他都是电路硬件/通信/电磁光电）
                                                                                                                                                [size=8.000000pt]ELEN E4903 Topic: Machine Learning ([size=8.000000pt]Spring ‘16[size=8.000000pt])[size=8.000000pt]
ELEN E6882 Topic: Visual Search Engine ([size=8.000000pt]Spring ‘12, ‘11[size=8.000000pt])
ELEN E6882 Topic: Mobile Sensing & Analysis ([size=8.000000pt]Spring ’16, ‘15[size=8.000000pt])
ELEN E6883 Topic: Detection & Estimation ([size=8.000000pt]Fall ‘10, ‘09, ‘08, ‘06[size=8.000000pt])
ELEN E6884 Topic: Data Compression ([size=8.000000pt]Spring ’14, ‘13, ‘12, ‘11[size=8.000000pt])
ELEN E6885 Topic: Reinforcement Learning ([size=8.000000pt]Fall ‘17[size=8.000000pt])
ELEN E6886 Topic: Sparse Rep. / High Dim. Geom. (Spr ’17, Fall ’15, . .)
ELEN E6887 Topic: Statistical Learning Theory ([size=8.000000pt]Spring ‘10, ‘09[size=8.000000pt])

[size=8.000000pt]ELEN E6889 Topic: Large Data Stream Proc. (Spr ’17, [size=8.000000pt]Fall ’15, Spr ’14, ‘10[size=8.000000pt])EECS E6890 Topic: Visual Recognition and Search ([size=8.000000pt]Spring ’14, ‘13[size=8.000000pt])
EECS E6891 Topic: Reproducing Computational Results ([size=8.000000pt]Spring ’14, ‘13[size=8.000000pt])
EECS E6892 Topic: Bayesian Models in Machine Learning ([size=8.000000pt]Fall ’15, Spring ‘14[size=8.000000pt])EECS E6893 Topic: Big Data Analytics ([size=8.000000pt]Fall ’17, ‘16, ‘15, ‘14[size=8.000000pt])
EECS E6894 Topic: Deep Learning for Computer Vision & NLP ([size=8.000000pt]Spring ’17, ‘15[size=8.000000pt])EECS E6895 Topic: Adv. Big Data Analytics ([size=8.000000pt]Spring ’17, ‘16, ‘15[size=8.000000pt])
EECS E6896 Topic: Quantum Computing and Comm. (Fall ’17)
EECS E6898 Topic: From Data to Solutions ([size=8.000000pt]Fall ’17, ‘16, Spr ’16, Fall ’12- ‘14[size=8.000000pt])

                                                                                                [size=8.000000pt]ELEN E4901 Topic: Mobile App Dev. w/ Android ([size=8.000000pt]Fall [size=8.000000pt]‘[size=8.000000pt]15[size=8.000000pt])ELEN E4904 Topic: Mobile Cloud ([size=8.000000pt]Fall ‘16[size=8.000000pt])
[size=8.000000pt]ELEN E4905 Topic: Cyber Security (Fall ’17)


剩下15学分可以选其他理工科（CS） 其中只能选3学分非工、科、数的（差不多一门课）


pros：
1. 名气响当当，综排无限好
2. 地址好，在纽约，可能可以去一些金融it公司
3. 有商学院，金融气氛好
4. EE排名比Brown好
5. 人脉好
cons：
1. 人多，据说EE因此比较水，而且就业peer pressure大
2. 生活费学费贵……是brown的1.6倍
3. 现在选CS的课似乎要填表，而且不知道抢不抢得到……毕竟人多

Brown:
分为thesis和non-thesis
thesis是两门数学+两门ECE+两门跟教授的research+三门随（C）意（S)，写论文才能毕业；
non-thesis是纯授课型，两门数学+三门ECE+三门cs
其中ECE的课程也有很多偏软：

[size=0.875em]ENGN 2520. Pattern Recognition and Machine Learning.

[size=0.875em]This course covers fundamental topics in pattern recognition and machine learning. We will consider applications in computer vision, signal processing, speech recognition and information retrieval. Topics include: decision theory, parametric and non-parametric learning, dimensionality reduction, graphical models, exact and approximate inference, semi-supervised learning, generalization bounds and support vector machines. Prerequisites: basic probability, linear algebra, calculus and some programming experience.

[size=0.875em]ENGN 2530. Digital Signal Processing.

[size=0.875em]An introduction to the basics of linear, shift invariant systems and signals and doing real processing of signal on a digital computer. Quantization and sampling issues are introduced. Discrete time and DFT properties, fast DFT algorithms, and spectral analysis are discussed. IIR and FIR digital filter design is a focus; stochastic and deterministic signals are introduced. MATLAB exercises are a significant part of the course.

[size=0.875em]ENGN 2540. Audio and Speech Processing.

[size=0.875em]ENGN 2560. Computer Vision.

[size=0.875em]ENGN 2912B. Scientific Programming in C++.

[size=0.875em]Introduction to the C++ language with examples from topics in numerical analysis, differential equations and finite elements. As a prerequisite, some programming knowledge, e.g., MATLAB projects. The course will cover the main C++ elements: data types; pointers; references; conditional expressions; streams; templates; Standard Template Library(STL); design and debugging techniques.

pros:
1. 小班化教学（ECE一届20多人），教学质量更高，选课更方便（这点很喜欢）
2. 研究生有office，每个人都有位置
3. 找工作peer pressure小，上一届ECE找工作70%去FLAG
4. 耗费便宜，在ivy中算是很实惠的


cons:
1. 综排专排都没有哥大好，国内名气一般
2. 没有商学院，想找金融工作的话大概比较难
3. 人太少了，人脉什么的可能比较差




诚心求比较……
以及现在找工作的情况据说是学校规模越大，人越多，越难找？因为career fair的录取名额是根据学分配的？据说因为这个所以usc和哥大的peer pressure非常大。。这点Brown好很多，求证实这一点




                               
                       
               
                               
                       
               

Brown CE也很水阿 两个project半斤八两 我投哥大一票 地理位置在这摆着

如果经济上没有大问题的话，还是Columbia吧。资源和机会都多很多。金融圈似乎很看中名校。而且如果最后回国的话，Columbia名声也好很多。

Vote for Columbia.基本上送分题

哈哈哈就是一直听说哥大EE非常水 所以有点纠结。。。

Vote for Columbia

大家都觉得哥大好吗。。。人太多了会不会水水的。。。

建议楼主去加在读的学生问实习情况，实习差不多就去哥大，求大米谢谢

如果ECE的话还是建议哥大吧，Brown的ESCE第一学期据说要上个很耗费时间的C++课，导致的很多人都没时间找实习，刷题。。。