Machine Learning for Data Science (CS4786)
Lecture 1
Tu-Th 8:40 AM to 9:55 AM
Klarman Hall KG70
Instructor : Karthik Sridharan
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Machine Learning for Data Science (CS4786) Lecture 1, Lecture notes of Machine Learning
Information about the course Machine Learning for Data Science (CS4786) at Cornell University. It includes details about the course webpage, TA office hours, placement exam, course grades, competitions, surveys, academic integrity, and data deluge. The document also mentions various applications of machine learning such as pedestrian detection, market predictions, spam classification, autocomplete, biometrics, recommendation systems, computer vision, and topic modeling.
Typology: Lecture notes
2018/2019
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Machine Learning for Data Science (CS4786)
Lecture 1
Tu-Th 8:40 AM to 9:55 AM Klarman Hall KG Instructor : Karthik Sridharan
Welcome the first lecture!
I know its really early!
Here
Here
But lets have fun!
COURSE INFORMATION
Course webpage is the official source of information: http://www.cs.cornell.edu/Courses/cs4786/2019sp Join Piazza: https://piazza.com/class/jr4fi8k75d571p TA office hours will start from week 2. Time and locations will be posted on info tab of course webpage Basic knowledge of python is required.
PLACEMENT EXAM!
Passing the placement exam is required to enroll Exam can be found at: http: //www.cs.cornell.edu/courses/cs4786/2019sp/hw0.html Upload your solutions in PDF format via the google form indicated in the exam page Score on the placement exam is only for feedback, does not count towards grades for the course.
ASSIGNMENTS
Total of 4 assignments. Each worth 7% of the grade Will be on Vocareum (using Jupyter notebook/python) Has to be done individually
EXAMS
(^1) Prelim On March 28th at STL Worth 20% of the grades. 2 Finals On May 14th (see schedule for details) Worth 20% of the grades.
SURVEYS
2 Surveys worth 1% each just for participation Survey will be anonymous (I will only have a list of students who participated) Important form of feedback I can use to steer the class Free forum for you to tell us what you want.
ACADEMIC INTEGRITY
(^1) 0 Tolerance Policy: no exceptions We have checks in place to look for violations in Vocareum (^2) If you use any source (internet, book, paper, or personal communication) cite it. 3 When in doubt cite.
Lets get started...
DATA DELUGE
Each time you use your credit card: who purchased what, where and when Netflix, Hulu, smart TV: what do different groups of people like to watch Social networks like Facebook, Twitter,... : who is friends with who, what do these people post or tweet about Millions of photos and videos, many tagged Wikipedia, all the news websites: pretty much most of human knowledge