Mathematical foundations of classification, regression, and decision making. Perceptron algorithm, logistic regression, and support vector machines. Numerical parameter optimization, including gradient descent and quasi-Newton methods. Expectation Maximization. Hidden Markov models and reinforcement learning. Principal Components Analysis. Learning theory including VC-dimension and PAC learning guarantees.
Credit hours: 4.0
Prerequisites: MATH 164, MATH 165, CSC 242 strongly recommended.
Class: Tuesdays and Thursdays 1105-1220 via zoom (link on blackboard).
Relevant Links: BlackBoard, Course Page, and Course Schedule.
We will meet synchronously via zoom at the scheduled class time. Class time will primarily be lecture format. Questions are highly encouraged. We will use breakout rooms for problem solving. Lecture portions will be recorded and posted to panopto. You will be required to either upload scans of written work or to typeset your assignments and exams using LaTeX and submit via PDF.
Textbook and Materials:
The anticipated schedule of topics is available at www.cs.rochester.edu/u/apurtee/246/schedule.pdf.
There will be two exams for this course -- both are take-home format. The midterm exam will be due by 1159pm anywhere-on-earth March 18th. The final exam will be due at the end of the final exam period, which is May 14th 1130am EST. Both exams will be distributed approximately 24 hours before their deadlines, and it is anticipated that you will complete the exam during whatever timeslot you typically would use to interact with the material. No lecture will be held on the day of the midterm.
Letter grades will follow the Official University of Rochester Grading Scheme. Note that the University scheme puts “average” somewhere between C and B.
|B: Above Average||>=83%|
|C-: Minimum satisfactory grade||>=70%|
|D: Minimum passing grade||>=60%|
Zoom, Cameras, and Recordings:
All lecture periods will be recorded and uploaded to Panopto as soon as possible. In practice, this may take a few hours after the lecture to complete. While you are encouraged to attend lecture synchronously, you are not required to do so.
Please note, students are not permitted to make their own recordings, either in-person or online. Please also note Section V.7 of the College’s Academic Honesty policy regarding “Unauthorized Recording, Distribution or Publication of Course-Related Materials.”
You will need access to a computer with reliable internet access for this course, but it does not need to be fast or higher bandwidth necessary than for standard Zoom calls. A computer with webcam capabilities (and enough hardware to run zoom while taking notes in your application of choice) is recommended. It is possible to complete all written work electronically using LaTeX; however, you may find it more convenient to have access to a scanner in order to capture and submit written work.
It is my hope that all participants in my courses feel welcome, respected, and supported. If anyone feels unfairly excluded for any reason, please let me know and I will work to bring the course into as equitable a state as possible.
All assignments and activities associated with this course must be performed in accordance with the University of Rochester’s Academic Honesty Policy. More information is available at: www.rochester.edu/college/honesty
All incidents of academic dishonesty will be reported. This is because academic dishonesty is harmful to the entire university community, AND because it is the explicit requirement of the University of Rochester Academic Honesty Policy.
All zyBook exercises must be completed on your own.
All quizzes must be completed on your own.
All projects must be completed on your own.
Note that posting homework and project solutions to public repositories on sites like GitHub is a violation of the College’s Academic Honesty Policy, Section V.B.2 “Giving Unauthorized Aid.”
The University of Rochester respects and welcomes students of all backgrounds and abilities. In the event you encounter any barrier(s) to full participation in this course due to the impact of disability, please contact the Office of Disability Resources. The access coordinators in the Office of Disability Resources can meet with you to discuss the barriers you are experiencing and explain the eligibility process for establishing academic accommodations. You can reach the Office of Disability Resources at: email@example.com; (585) 276-5075; Taylor Hall.
Students with an accommodation for any aspect of the course must make arrangements in advance through the Disability Resources office. Then, as instructed by the office, contact the instructor to confirm your arrangements.
This course follows the University policy regarding incompletes: “Incompletes may be given only when there are circumstances beyond the student’s control, such as illness or personal emergency, that prevented the student from finishing the course work on time.” Students who are unable to attend or complete any part of the course due to illness should contact the instructor as soon as possible. Please note that the University Health Service (UHS) does not provide retroactive excuses for missed classes. Students who are seen at UHS for an illness or injury can ask for documentation that verifies the date of their visit(s) to UHS without mention of the reason for the visit. For remote students, medical excuses will still require some kind of documentation from a healthcare provider. If you cannot visit UHS, please make arrangements with whoever you visit. Please contact CCAS or Disability Services if you need more help with this. Because of the potential impact across all of your courses, students with extended or severe illness should additionally contact the College Center for Advising Services (CCAS) for advice and assistance.
No late work will be accepted without prior approval from the instructor. If you are unable to complete your work on time, please submit what you have before the deadline in order to be considered for partial credit.
This course follows the College credit hour policy for four-credit courses, including lectures and study sessions.
Students are expected to do significant work outside of class time. You may find it necessary to spend several hours most weeks reading the textbook, studying derivations, and working on assignments.