Email: cjcarroll [at] usfca [dot] edu
Class Time: TR 10a-12p or 1p-3p in SFD 529
Office Hours: Tuesdays 3:30p-4:30p in person (SFD 529) and Mondays 12p-1p on Zoom (subject to change during quiz weeks - watch the course slack!)
Books: Pattern Recognition and Machine Learning. Bishop. Link
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Link
Deep Learning. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Link
Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman. Link
Probabilistic Machine Learning. Kevin Murphy. Link
*subject to change
| Week | Dates | Topics | Reading | Notable Events |
|---|---|---|---|---|
| Week 1 | 1/21 & 1/23 | Intro, SVD | Sec. 11.3 from MoMD; Sec. 3.4.1 from ESL | |
| Week 2 | 1/28 & 1/30 | PCA & Start Recommender Systems | Sec. 14.5.1 from ESL, Sec. 11.2 from MoMD, & Chapter 9 from MoMD | HW1 Due |
| Week 3 | 2/4 & 2/6 | Recommender Systems + PyTorch | Chapter 9 from MoMD | HW2 Due + Kaggle Checkpoint 1, Feb 7 @ 5p |
| Week 4 | 2/11 & 2/13 | AdaBoost + Gradient Boosting | Ch. 18 of PML; Ch. 10 of ESL | HW3 Due + Quiz 1 |
| Week 5 | 2/18 & 2/20 | AdaBoost + Gradient Boosting | Ch. 18 of PML; Ch. 10 of ESL | HW4 Due |
| Week 6 | 2/25 & 2/27 | Neural Networks & PyTorch | Ch. 13 of PML; Ch. 6 of DL | HW5 Due + Kaggle Checkpoint 2, Feb 28 @ 5p |
| Week 7 | 3/4 & 3/6 | Catch Up Day + Final Project Presentations | - | Final Kaggle Standings (Mar 2 @ 11:59p) + Project Report Due (Mar 3 @ 11:59p) + Presentations + Quiz 2 (Mar 6) |
| Week 8 | 3/11 & 3/13 | Spring Break | - | Freedom! |
On completion of this course the student should be able to:
- Describe and apply selected learning algorithms / models and their variants (see Course Content below).
- Select the appropriate learning algorithm or approach for a given situation or dataset.
- Implement machine learning algorithms from scratch in Python.
- Implement basic Neural Networks in PyTorch.
- Implement advanced feature engineering techniques.
As part of a team, the student will also carry out a machine learning project from start to finish, including:
- Researching literature related to the problem.
- Preparing the data for application of algorithms, including feature engineering.
- Choosing and applying appropriate ML algorithms (as well as hyper-parameter tuning).
- Evaluating and communicating results both orally and in writing.
Programming language is Python.
- Dimension Reduction: SVD and PCA
- Recommendation Systems: Collaborative Filtering, Matrix Factorization.
- Boosting: Adaboost, Gradient Boosting.
- Neural Networks, Pytorch.
- Applications of Neural Networks.
When in doubt, rely on the following:
- Put the work in & ask for help when stuck.
- Ask questions before spiraling.
- When confused, work with a partner & zoom into details.
- When you understand, teach others & zoom out to debrief.
- Use common sense whenever possible.
The class will be using Canvas & Github to distribute all resources.
Part of my job as an instructor is to assign grades fairly and in a manner that reflects the high academic standards at the University of San Francisco and in the MSDS program. Your grade in this course will be computed according to the following weights:
Attendance and Professionalism: 5%
Attendance is expected in all live lectures. Valid excuses for absence with permission will be accepted with documentation, but students are required to watch the lecture videos and submit class exercises and activities on Canvas/Github if any. Students who miss the live lectures with a valid excuse are required to submit the exercises within 24 hours of class time (3pm PST/PDT next day).
Professional behavior is expected both during classtime and outside of class when working with your fellow students. Issues with unprofessional behavior will result in deductions in your professionalism score.
Homework: 20%
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You will be assigned computational and theoretical homework assignments to be completed and turned in on Github every Friday before midnight at 11:59p Pacific time, with a 48 hour grace period. Github repositories will lock after this grace period ends.
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Students are encouraged to discuss and work together on assignments, but each student must turn in their own original work. If there is evidence that the work turned in is not original work, which includes copying another student’s homework or using any solutions found online, all credit for that homework set will be forfeited. Homework is not to be posted to online help sites. These sites will be checked frequently.
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No late homework past the grace period will be accepted.
Quizzes: 50%
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You will be required to complete 2 quizzes. All quizzes will be closed book. Details will be discussed in classes leading up to quiz dates.
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In order to pass this class, your average quiz grade for this section must be at least 60%.
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No make-up or early quizzes will be given in order to ensure fairness and integrity of the class. Missing an exam without proper documentation of a personal illness or family emergency will result in a score of zero for that exam. Any documentation must be submitted to the instructor before the exam in question at the earliest possible date.
Final Project 25%
The final project will be a computational group case study that brings together the techniques learned throughout the semester. The project description and groups will be published on Canvas. A final report of the project is due by Wednesday Mar 5 at 11:59p.
The MSDS program considers a grade of "A" to represent exceptional work with respect to both the instructor's expectations and peer student achievements. A grade of "B" represents the expected outcome, what is called "competence" in a business setting. A "C" grade represents achievements lower than the instructor's expectations for competence in the subject. A grade of "F" represents unacceptably low level of knowledge and understanding of subject matter. Scores less than 60% on Exams or less than 60% on the overall grade are considered "F" in this class.
As a Jesuit institution committed to cura personalis---the care and education of the whole person---the University of San Francisco has an obligation to embody and foster the values of honesty and integrity. The university upholds standards of honesty and integrity from all members of the academic community, including faculty, students, and staff. All students are expected to know and to adhere to the university's honor code. You can find the full text of the code online here. You are also bound by the terms of the MSDS Code of Conduct that you signed prior to matriculating in the analytics program. Refer to ON HOMEWORK sections for details regarding student collaboration on each category of deliverable. Plagiarism consists of copying any material from any source and submitting it as your own original work, regardless of where that material was sourced: the Internet, a book, textbook, or from deliverables previously submitted by other students. All students involved in any cheating or plagiarized deliverables, i.e., the cheater as well as the person(s) who willfully enabled or facilitated the act of cheating, will be reported to the MSDS Program Director. If you ever have questions about what constitutes plagiarism, cheating, or academic dishonesty in this course, I am happy to discuss these topics with you.
If you are a student with a disability or disabling condition, or if you think you may have a disability, please contact USF Student Disability Services (SDS) at 415.422.2613 within the first week of class, or immediately upon onset of the disability, to speak with a disability specialist. If you are determined eligible for reasonable accommodations, please meet with your disability specialist so they can arrange to have your accommodation letter sent to me, and we will discuss your needs for this course. For more information, please visit this link or call 415.422.2613. Accommodations are not retroactive.
All students are expected to behave in accordance with the Student Conduct Code and University policies (see here. Open discussion and disagreement is encouraged when done respectfully and in the spirit of academic discourse. There are also a variety of behaviors that, while not against a specific University policy, may create disruption in this course. Students whose behavior is disruptive or who fail to comply with the instructor may be dismissed from the class for the remainder of the class period and may need to meet with the instructor or Dean prior to returning to the next class period. If necessary, referrals may also be made to the Student Conduct process for violations of the Student Conduct Code.
If you fall ill or have an emergency (personal or otherwise) that significantly affects your ability to complete a project or take an exam, you must notify the instructor before the task or artifact is due. Do not simply skip an exam or an assignment and say you were sick after the fact. Always make arrangements with the instructor beforehand, rather than declaring illness or emergency later. Accommodations are not retroactive. Illness and emergency related situations must be disclosed to both the instructor and program director in writing. Illness-related issues must be accompanied by a doctor’s note.
The Learning & Writing Center provides assistance to all USF students in pursuit of academic success. Peer tutors provide regular review and practice of course materials in the subjects of Math, Science, Business, Economics, Nursing and Languages. Other content areas can be made available by student request. To schedule an appointment, log on to TutorTrac here. Students may also take advantage of writing support provided by Rhetoric and Language Department instructors and academic study skills support provided by Learning Center professional staff. For more information about these services contact the Learning & Writing Center at 415.422.6713, lwc /at/ usfca /dot/ edu, or stop by Cowell 215. Information may also be found here.
Our diverse staff offers individual, couple, and group counseling to student members of our community. Services are confidential and free of charge. Call 415.422.6352 for an initial consultation appointment. Telephone consultation after hours is available between the hours of 5:00 PM to 8:30 AM; call the above number and press 2.
As an instructor, one of my responsibilities is to help create a safe learning environment on our campus. I also have a mandatory reporting responsibility related to my role as a faculty member. I am required to share information regarding sexual misconduct or information about a crime that may have occurred on USF's campus with the University. Here are other resources:
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To report any sexual misconduct, students may visit the Office of Student Conduct, Rights and Responsibilities (UC 5th floor, 415.422.5330 or see other options by visiting the website
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Students may speak to someone confidentially, or report a sexual assault confidentially by contacting Counseling and Psychological Services at 415.422.6352.
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For an off-campus resource, contact San Francisco Women Against Rape at 415.647.7273 / www.sfwar.org.