Introduction to Machine Learning

CMSC 478

Spring 2023

Contact Information

Instructor:

TA:

Grader:

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

Grades will be based on a midterm exam, a final exam, and seven homework assignments. The homeworks are crucial for solidifying what you learn in class.

The weights on the various items are as follows:

Grades will be assigned as follows based on your class average:

Note that, for example, [80, 90) means the interval that includes 80 but not 90.

All assignments will be submitted to the TA via slack.

Late Policy

All assignments must be turned in by 11:59PM Eastern time on the date that they are due. I understand that students have many demands on their time that vary in intensity over the course of the semester. Therefore, you will be allowed 5 late days without penalty for the entire semester. You can turn in 5 different assignments one day late each, or one assignment 5 days late, and so on. Late days cannot be used for exams.

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 by 11:59PM Eastern on the day that it is due. It is late by two days if I do not have it by 11:59PM Eastern the following day, and so on. It is your responsibility to keep track of how many late days you have used.

Academic Honesty

By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC’s scholarly community in which everyone’s academic work and behavior are held to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal. To read the full Student Academic Conduct Policy, consult UMBC policies, or the Faculty Handbook (Section 14.3). For graduate courses, see the Graduate School website.

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 can be a zero on the assignment, depending on the severity. The penalty for the second instance can be an F in the class, depending on the severity.

Accessibility and Disability Accommodations, Guidance and Resources (required)

Accommodations for students with disabilities are provided for all students with a qualified disability under the Americans with Disabilities Act (ADA & ADAAA) and Section 504 of the Rehabilitation Act who request and are eligible for accommodations. The Office of Student Disability Services (SDS) is the UMBC department designated to coordinate accommodations that creates equal access for students when barriers to participation exist in University courses, programs, or activities.

If you have a documented disability and need to request academic accommodations in your courses, please refer to the SDS website at sds.umbc.edu for registration information and office procedures.

SDS email: disAbility@umbc.edu

SDS phone: 410-455-2459

If you will be using SDS approved accommodations in this class, please contact the instructor to discuss implementation of the accommodations. During remote instruction requirements due to COVID, communication and flexibility will be essential for success.

Sexual Assault, Sexual Harassment, and Gender Based Violence and Discrimination (required)

UMBC Policy and Federal law (Title IX) prohibit discrimination and harassment on the basis of sex, sexual orientation, and gender identity in University programs and activities. Any student who is impacted by sexual harassment, sexual assault, domestic violence, dating violence, stalking, sexual exploitation, gender discrimination, pregnancy discrimination, gender-based harassment or retaliation should contact the University’s Title IX Coordinator to make a report and/or access support and resources:

Jackie Moran, Title IX Coordinator and Interim Director

410-455-1717, jmoran5@umbc.edu

You can access support and resources even if you do not want to take any further action. You will not be forced to file a formal complaint or police report. Please be aware that the University may take action on its own if essential to protect the safety of the community.

If you are interested in or thinking about making a report, please use the Online Reporting/Referral Form. Please note that, if you report anonymously, the University’s ability to respond will be limited.

Notice that Faculty are Responsible Employees with Mandatory Reporting Obligations:

All faculty members are considered Responsible Employees, per UMBC’s Policy on Sexual Misconduct, Sexual Harassment, and Gender Discrimination. Faculty are therefore required to report any/ all available information regarding conduct falling under the Policy and violations of the Policy to the Title IX Coordinator, even if a student discloses an experience that occurred before attending UMBC and/or an incident that only involves people not affiliated with UMBC. Reports are required regardless of the amount of detail provided and even in instances where support has already been offered or received.

While faculty members want encourage you to share information related to your life experiences through discussion and written work, students should understand that faculty are required to report past and present sexual assault, domestic and interpersonal violence, stalking, and gender discrimination that is shared with them to the Title IX Coordinator so that the University can inform students of their rights, resources and support. While you are encouraged to do so, you are not obligated to respond to outreach conducted as a result of a report to the Title IX Coordinator.

If you need to speak with someone in confidence, who does not have an obligation to report to the Title IX Coordinator, UMBC has a number of Confidential Resources available to support you:

Other Resources:

Child Abuse and Neglect:

Please note that Maryland law and UMBC policy require that faculty report all disclosures or suspicions of child abuse or neglect to the Department of Social Services and/or the police even if the person who experienced the abuse or neglect is now over 18.

Textbook

We will use a variety of online sources during this course.

Tools

In most cases you will be required to use python for homeworks because python has become the default language for machine learning at scale. Therefore, all of the examples that I do in class where we run an actual algorithm will be done using scikit-learn. A very easy way to get everything you may need is to install anaconda. It has python, scikit, and Jupyter notebooks for working with data and presenting results.

Syllabus

This syllabus is subject to small changes, but due dates and exam dates will not change. Note that for each topic there will be two sets of readings - some that everyone should do (marked all) and some that are optional (marked opt). You will only be held responsible for what is in the readings that everyone does, but if you want more information the optional readings are a good source.

Class
Date
Topic
Events/Readings
1 Mon  30 Jan Course overview. What is machine learning? Read Chapter 1 of my book
2 Wed  01 Feb Loss functions, gradient descent Read Chapter 2 of my book
3 Mon  06 Feb Perceptrons Homework 1 in HTML and Jupyter form
4 Wed  08 Feb Geometry of hyperplanes
5 Mon  13 Feb Decision trees (slides, reading) (chapter 3.1 - 3.4, 3.7) Homework 1 due; Homework 2 out
6 Wed  15 Feb Logistic regression Slides
7 Mon  20 Feb Clustering Slides
8 Wed  22 Feb Nearest neighbors CIML chapter 3 through 3.4
9 Mon  27 Feb Methodology Slides
Homework 2 due; Homework 3 out, mushroom data
10 Wed  01 Mar More methodology
11 Mon  06 Mar Reinforcement learning Chapter 3 and 6.5 of the RL Book
12 Wed  08 Mar More reinforcement learning
13 Mon  13 Mar A little more reinforcement learning Homework 3 due
14 Wed  15 Mar Kernel methods Slides
Mon  21 Mar Spring Break
Wed  22 Mar Spring Break
15 Mon  27 Mar Support vector machines Homework 4 out
16 Wed  29 Mar Midterm review
17 Mon  03 Apr Midterm Exam on content of classes 1 - 14
18 Wed  05 Apr Support vector machines Homework 4 due
19 Mon  10 Apr Neural networks (Slides) Homework 5 out
20 Wed  12 Apr More NNs
21 Mon  17 Apr Convolutional neural networks Slides
22 Wed  19 Apr More CNNs Homework 5 due; Homework 6 out
23 Mon  24 Apr Recurrent neural networks Slides
24 Wed  26 Apr More RNNs
25 Mon  01 May Graphical models
26 Wed  03 May Bayes nets (Slides) Homework 6 due; Homework 7 out
27 Mon  08 May Expectation maximization Slides
28 Wed  10 May Dimensionality reduction
29 Mon  15 May Final exam review Homework 7 due
Fri  19 May Final Exam 10:30AM - 12:30PM