Introduction to Machine Learning

Contact Information

TA: Mohammad Eskandari, eskandari@umbc.edu
Office hours: Thursdays 10:00am - 1:00pm

We will rely on Slack for asynchronous communication. Please use this link to sign up for the class Slack. You can use Slack for discussions between yourselves and to ask questions of me or the TA. If you want to ensure that I see your post, please use @oates in it so that I get a notification. Most discussions tend to take place in the general channel, but feel free to ask me to create other channels.

Grading

The weights on the various items are as follows:

• exams - 15% each, 30% total
• homeworks - 10% each, 50% total
• project - 20%

I will use plus/minus grading. Grades will be assigned as follows based on your class average:

• [93, 100] = A
• [90, 93) = A-
• [87, 90) = B+
• [83, 87) = B
• [80, 83) = B-
• [77, 80) = C+
• [73, 77) = C
• [70, 73) = C-
• [60, 70) = D
• [0, 60) = F

Professor Oates grades the exams and projects, and the TA grades the homeworks. All assignments will be submitted to the person grading them via slack. If you have questions about grading on homeworks, ask the TA first. If the question cannot be resolved that way, ask Professor Oates. All questions about grades on a homework must be dealt with before grades on the next homework are out.

Late Policy

Once the late days are used, a penalty of 33% will be imposed for each day (or fraction thereof) an assignment is late (33% for one day, 66% for two, 100% for three or more). An assignment is late by one day if it is not turned in at beginning of class on the day that it is due. It is late by two days if I do not have it by 2:30pm the following day, and so on. It is your responsibility to keep track of how many late days you have used.

If something arises, like a serious illness, and you'll need more time with an assignment, let Professor Oates know before the assignment is due. But note that the late days are meant to be used precisely for things like minor illnesses.

Masking

Project

Projects may be done by individuals or teams of two people. However, teams of two will be expected to do significantly more work than what is expected of an individual project. More information on projects can be found here.

Academic Honesty

I will actively monitor all student work, as will the TA, for instances of academic misconduct. The penalty for any such misconduct on the first instance will be a zero on the assignment. The penalty for the second instance will be an F in the class. I will report all instances of academic misconduct to the graduate school.

Textbook

• ESL: Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf
• ITILA: Information Theory, Inference and Learning Algorithms: http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
• UML: Understanding Machine Learning: From Theory to Algorithms: https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
• CIML: A Course in Machine Learning: http://ciml.info/

Tools

Syllabus