You should be familiar with the following topics:
The requirements of the course are as follows:
Biweekly problem sets (70% of grade).
Your solutions must be typed and prepared as LaTeX documents (instructions will be given on how to use LaTeX.) The (more or less) biweekly problem sets will be due by 11:59pm (New York City current time) on each due date unless otherwise indicated on the assignments page. You must submit a pdf file of your HW solutions on Gradescope by 11:59pm on the due date or your homework will be considered late (details about HW submission can also be found on the assignments page).
You are allowed a total of five late days for the semester. Each late day is exactly 24 hours; late days cannot be subdivided -- five minutes late counts as one late day. Note also that problem sets cannot be subdivided with respect to late days (i.e. you cannot use 1/6 of a late day on a 6-problem problem set by handing in one problem one day late). Late days over and beyond the allotted five late days will be penalized at a rate of 10% of the initial grade per late day (with the same system in place; five minutes late for one problem counts as one late day for the entire set). Thus, for example, a problem set which is four days late after all five late days have been used would receive 60% of the points that it would have received had it been turned in on time.
No problem sets will be accepted after ten days have elapsed from the initial due date. Additionally, for the first problem set, which is due Thurs Sept 29 2022, no late submissions will be accepted after Thurs Oct 6 2022 at 11:59pm, as solutions to problem set 1 will be released then (so that you can have a week to digest the solutions to problem set 1 before problem set 2 is due).
For any exceptions to any of the above, you must have your undergraduate advisor (for undergrads) or your graduate advisor (for graduate students) contact me.
Some problems will be challenging; you are advised to start the problem sets early.
Midterm evaluation (10% of grade).
Details TBA, but the midterm will be on Thurs Oct 20 2022.
Final evaluation (20% of grade).
Details TBA. The final evaluation will be cumulative—anything covered this semester will be fair game unless the instructor explicitly says that it will not be on the evaluation. The best way to prepare for the evaluation is to go over course notes, readings, homework problems and solution sets.
You are encouraged to discuss the course material and the homework problems with each other in small groups (2-4 people; 4 is an upper limit), but you must list all discussion partners on your problem set. Discussion of homework problems may include brainstorming and verbally discussing possible solution approaches, but must not go as far as writing up solutions together; each person MUST WRITE UP HIS/HER SOLUTIONS INDEPENDENTLY. You may not collaborate with another student on writing up solutions or even look at another student's written solutions. If your homework writeup resembles that of another student in a way which suggests that you have violated the above policy, you may be suspected of academic dishonesty.
You may consult certain outside materials, specifically lecture notes and videos of other classes, any textbooks, and research papers. You may not consult any other materials, including solved homework problems for this or any other class. For all outside materials used, you must provide a detailed acknowledgement of the precise materials used. Whether or not you consult outside materials, you must always write up your solutions in your own words. If your homework writeup resembles any outside source in a way which suggests that you have violated the above policy, you may be suspected of academic dishonesty.
Violation of any portion of this policy will result in a penalty to be assessed at the instructor's discretion. This may include receiving a zero grade for the assignment in question AND a failing grade for the whole course, even for the first infraction.
More generally, students are expected to adhere to the Academic Honesty policy of the Computer Science Department; this policy can be found in full here. Please contact the instructor with any questions.
If you dispute the grade received for an assignment, you must submit, in writing through Gradescope, a detailed and clearly stated argument for what you believe is incorrect and why. This must be submitted no later than one week after the assignment was returned. (For example, if the assignment were returned to the class on Tuesday, your regrade request would have to be submitted before the next Tuesday.) Requests for a re-grade after this time will not be accepted. A written response will be provided within one week indicating your final score. Requests of re-grade of a specific problem may result in a regrade of the entire assignment. This re-grade and written response is final. Keep in mind that a re-grade request may result in the overall score for an assignment being lowered.
The textbook for this course is:
This book is available for purchase on-line. It's also available on reserve in the science and engineering library, and is electronically available through the Columbia library here (you will need to be signed in to access this). Note that several topics which we'll cover (particularly early in the semester) are not in the book. Here are some good sources for some of the material which we will cover in the first few weeks of class:
An overview of Chernoff and Hoeffding bounds can be found here (thanks to Clement Canonne for preparing this).
For the unit on boosting, a good reference is Rob Schapire's survey paper "The Boosting approach to machine learning: An overview". The first three sections give a (very condensed) treatment of the AdaBoost algorithm we'll cover in class, and later sections have good overviews of more advanced material. The original paper on Adaboost, "A decision-theoretic generalization of on-line learning and an application to boosting", is a good sources as well. Click here for a handy copy of the AdaBoost algorithm.