Introduction to Computer Science Research
Course Description:
This course provides students with an introduction to research in the fields of computer science, including but not limited to machine learning, data science, and cybersecurity. Explores how the scientific method is applied to these fields, covers the breadth of sub-areas of speciality that exist, and gives students practice on how to locate and read scientific literature in different sub-areas. Also provides students with an overview of graduate education in these fields.
Course Details:
- Lecturer: Professor Qi (Rose) Yu (roseyu@northeastern.edu)
- TA: Giridhari Venkatadri (venkatadri.g@husky.neu.edu)
- Time: 10:30 am - 11:35 am M (Lecture) W (Lab)
- Location: Snell Library 111
- Piazza: piazza.com/northeastern/fall2019/cs3950
- Office Hours: email to schedule appointments
Prerequisite:
- CS 2500
- Basic knowledge with linear algebra, statistics, optimization
- Familiar with programming in Python
Course Assessment:
This course will be largely discussion-based, and you will be expected to actively participate in class.
- 25% Homework assignments
- 35% Mini research project
- 20 % Paper presentation
- 20% Class participation
NOTES: For students who need computing resources for the class project, we recommend you to look into AWS educate program for students. You’ll get 100 dollar’s worth of sign up credit.
Course Syllabus
Note: the syllabus is tentative and is subject to change.
[1] Dean Keith Simonton, After Einstein: Scientific genius is extinct, Nature 493, 602 (31 January 2013)
[2] Alan Mathison Turing, Computing Machinery and Intelligence, Mind 49, 433-460
[3] Brown, Noam, and Tuomas Sandholm. Superhuman AI for multiplayer poker. Science 365.6456 (2019): 885-890.
[4] Brynjolfsson, Erik, and Tom Mitchell. What can machine learning do? Workforce implications. Science 358.6370 (2017): 1530-1534.
[5] Bernard Chazelle. The Algorithm: Idiom of Modern Science.
[6] Avi Wigderson. Math and Computation. Chapter 2-3.
[7] L. Pinto and A. Gupta, Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours, 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016.
[8] Timothy P. Lillicrap∗ , Jonathan J. Hunt∗ , Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver & Daan Wierstra, Continuous control with deep reinforcement learning. International Conference on Learning Representations (ICLR), 2016