CMSC 478 — Introduction to Machine Learning
Fall 2023
- CMSC 478 — Introduction to Machine Learning
Logistics
- Instructor: KMA Solaiman, ksolaima@umbc.edu
- Teaching assistant: Surya Kiran Challagulla, ru17699@umbc.edu
- Lecture time: TTh 2:30-3:45pm (01), TTh 5:30-6:45pm (02)
- Location:
SONDHEIM 203(01),SONDHEIM 105(02) - Credit Hours: 3.00
- Q&A, Course discussion and announcements: Campuswire
- For any sensitive issue, please email me (ksolaima@umbc.edu), preferrably with a subject preceded by
CMSC478-concern. - Exam and assignment submissions: Blackboard
- Office hours
Thu 1-2pm, or by appointment(more if needed), ITE 201-C, KMA SolaimanThu 3-5pm, or by appointment, ITE 334, Surya Kiran Challagulla
Note: Visit Blackboard for instructions on joining Campuswire.
Course Description
This course serves as a foundation to apply machine learning techniques in different domains and for more advanced learning problems. The topics covered will include supervised learning, unsupervised learning, reinforcement learning, weakly or self-supervised learning, Generalization and Regularization, and Ensemble Learning.
The goals for this course are:
- be introduced to some of the core problems and solutions of ML;
- learn different ways that success and progress can be measured in ML;
- be exposed to how these problems relate to those in computer science and subfields of computer science;
- have experience experimenting with ML approaches;
- identify issues with features, data, and how to handle them;
Textbooks
Hal Daume from UMCP has written a good text for an introductory course in machine learning. You can get it here: CIML. Another good source is Machine Learning by Tom Mitechell. There are also some lecture notes from Stanford ML group. We will use some parts of that.
There are a few other online texts that we’ll draw from or I will give you specific lecture notes. You’ll find links to them below in the schedule of topics, with specific sections that you should read.
Prerequisites
This is an upper-level undergraduate level Computer Science course and we will assume that you will have a good grounding in algorithms, statistics, and adequate programming skills (CMSC 341). Many of the homework assignments will involve programming and you will be expected to do them in Python. Having said that, we will try our best to provide materials or backgrounds for the programming assignments.
Course Schedule
This syllabus and schedule is preliminary and subject to change. Abbreviations refer to the following:
- CIML: Book by Hal Daume
- TM: Tom Mitchell
- SML: Lecture Notes from Stanford ML
- LN: Lecture Notes, CMSC478 by KMA Solaiman
| Date | Topics | Notes | Readings |
|---|---|---|---|
| Week 1 | |||
| Thu Aug 31 | Course Overview. Administrivia and What is ML? | 1. YOLO Object Detection 2. 10 years of training in 10 days for learning to be a warrior | |
| Week 2 | Supervised Learning | ||
| Tue Sep 05 | Supervised learning setup, LMS | SML Part 1, Chap 1, 1.1 | |
| Thu Sep 07 | Weighted Least Squares, Perceptron, Logistic regression, Newton’s Method | SML Chap 1.3, Chap 2, See more: Chap 1.4 | |
| Week 3 | |||
| Tue Sep 12 | Multi-class classification | SML Chap 2.3 | |
| Generative Learning Models | |||
| Thu Sep 14 | Density estimation. Bayes Rule. MLE vs MAP | HW1 is out on BB and due on 09/25 | Estimating Probabilities |
| Week 4 | |||
| Tue Sep 19 | Naive Bayes, Conditional Models. | Mitchell-Notes Chapter 3 | |
| Thu Sep 21 | Naive Bayes, Gaussian Naive Bayes | Continuous Distributions, Discrete Distributions | |
| Week 5 | |||
| Tue Sep 26 | Gaussian Naive Bayes, Laplace Smoothing | Mitchell-Notes Chapter 3, SML Chapter 4, LN Section 1 | |
| Generalization and Regularization | |||
| Thu Sep 28 | Bias - Variance Tradeoff | SML Chapter 8.1 | |
| Week 6 | |||
| Tue Oct 03 | Proposal Overview. | ||
| Thu Oct 05 | Regularization. Feature / Model selection. Evaluation Metrics. | Project Proposal Due on Oct 7 | SML Chapter 9.1, 9.3 |
| Week 7 | |||
| Tue Oct 10 | Kernels | SML Chapter 5 | |
| Thu Oct 12 | Support Vector Machine | A guide to SVM | |
| Week 8 | |||
| Tue Oct 17 | Midterm Review | ||
| Thu Oct 19 | Midterm Exam | ||
| Week 9 | Neural Networks | ||
| Tue Oct 24 | Kernel SVM, Neural Network | ||
| Thu Oct 26 | Neural Network: Backpropagation | Chapter 10 of CIML, SML 7.2 | |
| Week 10 | |||
| Tue Oct 31 | NN-slide1, NN-slide2 | Practice Colab Notebooks | |
| Thu Nov 02 | CNN, K-Means | Optional: Transformers in Detail | |
| Week 11 | Unsupervised Learning | ||
| Tue Nov 07 | K-Means, Self-study:KNN | Lecture Video | CIML Chapter 3 |
| Thu Nov 09 | No class | ||
| Week 12 | |||
| Tue Nov 14 | PCA, ICA | PCA in action, Choosing K | Chapter 15 section 2 of CIML for PCA |
| Thu Nov 16 | PCA contd. | ||
| Week 13 | |||
| Tue Nov 21 | Ensemble Learning, Decision Tree | Lecture Video | TM Chapter 3, CIML Chap 13 |
| Thu Nov 23 | Thanksgiving Day No Classes | ||
| Week 14 | |||
| Tue Nov 28 | Reinforcement Learning | Q-Learning Example | |
| Thu Nov 30 | RL: Value Iteration, Policy Iteration | If you are interested to learn more: check these - chapter 4, 5, 8.11, Monte Carlo Tree Search | SML Chapter 15.1 - 15.4 |
| Week 15 | Projects | ||
| Tue Dec 05 | Project Presentations | Project Presentation Schedule: Sheet 1 and Sheet 3 | Quiz |
| Thu Dec 07 | Project Presentations | Project Presentation Schedule: Sheet 2 and Sheet 4 | |
| Week 16 | |||
| Tue Dec 12 | Final Exam Review | Review-slides-01, Review-slides-02 | Lecture Video |
| Thu Dec 14 | No Classes | ||
| Week 17 | |||
| Sun Dec 17 | Project Final Report Due | ||
| Wed Dec 20 | Final Exam |
Assignments
TBA
Midterm and Final Exams
The material covered by the exams will be drawn from assigned readings in the text, from lectures, and from the homework. Material from the readings that is not covered in class is fair game, so you are advised to keep up with the readings.
Projects
The project is meant to give students deeper exposure to some topic in machine learning than they would get from the lectures, readings, and discussions alone. Those projects that are most successful often blend the student’s own research with machine learning, e.g. by applying machine learning techniques to a problem in some other area, or by bringing an insight from some other area to a problem in machine learning. However, projects need not involve ties with ongoing research. Many good projects in the past have investigated the application of existing algorithms to a domain/dataset of interest to the student, such as Texas Hold’em, the stock market, sporting events, and so on. Students can come up with their own project ideas or they can come see me and we’ll brainstorm project ideas.
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.
Course Evaluation
Grades will be based on your performance in homework assignments, project, a mid-term examination and a final examination. There will be 5-10 homeworks throughout the semester. The overall evaluation is as follows:
| Component | % |
|---|---|
| Homework/Programming Assignments | 50% |
| Exams | 15% each, 30% |
| Project | 20% |
“Course engagement” consists of, e.g., asking questions, participating in discussions (in class or online), responding to surveys or checkpointing questions, etc.). “Course engagement” could count in your favor in borderline cases.
As per University policy, incompletes will be granted only under extraordinary circumstances; students who are enrolled after the last day to drop a class should be prepared to receive a grade of A-F.
Grading Scale: The following grading scale is used on the normalized final, rounded percentages:
| If you get at least a/an… | you are guaranteed a/an… or higher |
|---|---|
| 90 | A |
| 80 | B |
| 70 | C |
| 60 | D |
| 0 | F |
Policies
If you have extenuating circumstances that result in an assignment being late, please talk to me as soon as possible.
Due Dates
Due dates will be announced on the course website and Campuswire. Unless stated otherwise, items are due by 11:59 PM (UMBC time) of the specified day. Submission instructions will be provided with each assigned item.
Extensions and Late Policy
Personal or one-off extensions will not be granted. Instead, everyone in this course has ten (10) late days (3 days maximum for each assignment) to use as needed throughout the course. These are meant for personal reasons and emergencies; do not use them as an excuse to procrastinate. Late days are measured in 24 hour blocks after a deadline. They are not fractional: an assignment turned in between 1 minute and 23 hours, 59 minutes (1,439 minutes) after the deadline uses one late day, an assignment turned in between 24 hours and 47 hours, 59 minutes (2879) after the deadline uses two late days, etc. The number of late days remaining has no bearing on assignments you turn in by the deadline; they only affect assignments you turn in after the deadline. If you run out of late days and do not turn an assignment in on time, please still complete and turn in the assignments. Though late assignments after late days have been exhausted will be recorded as a 0, they will still be marked and returned to you. Morever, they could count in your favor in borderline cases. There is a hard cutoff of the final exam block. Late days cannot be used beyond this time. I reserve the right to issue class-wide extensions.
Academic Honesty
Do not cheat, deceive, plagiarize, improperly share, access or use code, or otherwise engage in academically dishonest behaviors. Doing so may result in lost credit, course failure, suspension, or dismissal from UMBC. Instances of suspected dishonesty will be handled through the proper administrative procedures.
We will follow a policy described in this statement adopted by UMBC’s Undergraduate Council and Provost’s Office.
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.
Especially for computer science classes, there are generally questions about what is and is not alloThu. You are encouraged to discuss the subject matter and assignments with others. The Campuswire discussion board provides a great forum for this. However, you may not write or complete assignments for another student; allow another student to write or complete your assignments; pair program; copy someone else’s work; or allow your work to be copied. (This list is not inclusive.)
As part of discussing the assignments, you may plan with other students; be careful when dealing with pseudocode. A good general rule is that if anything is written down when discussing the assignments with others, you must actually implement it separately and you must not look at your discussion notes.
You are free to use online references like Stack Overflow for questions that are not the primary aspect of the course. If, for example, you’re having an issue with unicode in Python, or are getting a weird compilation error, then sites like Stack Overflow are a great resource. Don’t get stuck fighting your tools.
You may generally use external libraries (and even parts of standard libraries), provided what you use does not actually implement what you are directed to implement.
Generative AI: For this class, if you use ChatGPT (or similar chatbots or AI-based generation tools), you must describe exactly how you used it, including providing the prompt, original generation, and your edits. This applies to prose, code, or any form of content creation. Not disclosing is an academic integrity violation. If you do disclose, your answer may receive anywhere from 0 to full credit, depending on the extent of substantive edits, achievement of learning outcomes, and overall circumvention of those outcomes.
Use of AI/automatic tools for grammatical assistance (such as spell-checkers or Grammarly) or small-scale predictive text (e.g., next word prediction, tab completion) is okay. Provided the use of these tools does not change the substance of your work, use of these tools may be, but is not required to be, disclosed.
Be sure to properly acknowledge whatever external help—be it from students, third party libraries, or other readings—you receive in the beginning of each assignment. Please review this overview of how to correctly cite a source and these guidelines on acceptable paraphrasing.
Accomodations
Students with Accommodation Needs
The Office of Student Disability Services (SDS, https://sds.umbc.edu) works to ensure that students can access and take advantage of UMBC’s educational environment, regardless of disability. From the SDS,
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 require the use of SDS-approved accommodations in this class, please make an appointment with me to discuss the implementation of the accommodations.
Religious Observances & Accommodations UMBC Policy provides that students should not be penalized because of observances of their religious beliefs, and that students shall be given an opportunity, whenever feasible, to make up within a reasonable time any academic assignment that is missed due to individual participation in religious observances. It is the responsibility of the student to inform me of any intended absences or requested modifications for religious observances in advance, and as early as possible.
Sexual Assault, Sexual Harassment, and Gender-based Violence and Discrimination
UMBC Policy in addition to federal and state law (to include Title IX) prohibits 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 related retaliation should contact the University’s Title IX Coordinator to make a report and/or access support and resources. The Title IX Coordinator can be reached at titleixcoordinator@umbc.edu or 410-455-1717.
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 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.
Faculty Reporting Obligations
All faculty members and teaching assistants are considered Responsible Employees, per UMBC’s Policy on Sexual Misconduct, Sexual Harassment, and Gender Discrimination. So please note that as instructors, I, other faculty members, and the teaching assistants are required to report all known information regarding alleged conduct that may be a violation of the Policy to the University’s 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 to 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 harassment, sexual assault, domestic and dating 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:
Retriever Integrated Health (Main Campus): 410-455-2472; Tueday – Friday 8:30 a.m. – 5 p.m.; For After-Hours Support, Call 988.
Pastoral Counseling via The Gathering Space for Spiritual Well-Being: 410-455-6795; i3b@umbc.edu; Tueday – Friday 8:00 a.m. – 10:00 p.m.
For after-hours emergency consultation, call the police at 410-455-5555
Other Resources:
Women’s Center (open to students of all genders): 410-455-2714; womenscenter@umbc.edu; Tueday – Thursday 9:30 a.m. – 5:00 p.m. and Friday 10:00 a.m. – 4 p.m.
Child Abuse and Neglect
Please note that Maryland law and UMBC policy require that I 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.
Hate, Bias, Discrimination, and Harassment
UMBC values safety, cultural and ethnic diversity, social responsibility, lifelong learning, equity, and civic engagement.
Consistent with these principles, UMBC Policy prohibits discrimination and harassment in its educational programs and activities or with respect to employment terms and conditions based on race, creed, color, religion, sex, gender, pregnancy, ancestry, age, gender identity or expression, national origin, veterans status, marital status, sexual orientation, physical or mental disability, or genetic information.
Students (and faculty and staff) who experience discrimination, harassment, hate, or bias based upon a protected status or who have such matters reported to them should use the online reporting/referral form to report discrimination, hate, or bias incidents. You may report incidents that happen to you anonymously. Please note that, if you report anonymously, the University’s ability to respond may be limited.
Acknowledgements
This class borrows inspirations from several incredible sources. The project description is inspired by my colleague, Tim Oates. Some of the lecture slides’ material will be adapted from Machine Learning course offered by Stanford.