Instructor: Jiayu Zhou [email]
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Time: Monday and Wednesday 10:20am - 11:40am
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Location: Engineering Building, EB 1230
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Office Hours: Monday and Wednesday 11:40am - 12:40pm, EB 2134
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Textbook: Learning from Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, 2012. Webpage
Teaching Assistant: Qi Wang
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Office Hours: Tuesday 9:00am - 10:00am and by appointments.
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Location: EB 3203
An introduction to the field of machine learning, including linear models for regression and classification, generative models, support vector machines and kernel methods, neural networks and deep learning, decision trees, unsupervised learning and dimension reduction. (3 credits)
Student learning outcomes include (1) understanding the foundation, major techniques, applications, and challenges of machine learning; (2) the ability to apply basic machine learning algorithms for solving real-world problems. The learning outcomes will be assessed based on a combination of homework assignments and exams.
In this course you will be extensively involved a variety of math topics, especially, linear algebra and matrix computation, basics about probability theory and numerical optimization. An online quiz will be available before the class for self-assessment.
You will be using Python for programming.
- Introduction
- Linear algebra review
- Linear regression
- Logistic regression
- Probability review
- Basic probability and naïve Bayes classifier
- Generative versus discriminative models
- Generalization and overfitting
- Support vector machines and kernel methods
- Neural networks
- Deep learning
- Decision tree and random forests
- Dimension reduction, principal component analysis (PCA) • Unsupervised learning and K-means clustering
- Spectral clustering
Announcements will be emailed to the course mailing list. A welcome note will be sent to the mailing list at the beginning of the semester. If you do not receive the welcome message before the first class, please send mail to me.
- Homework (6): 35%
- Exam 1: 20%
- Exam 2: 20%
- Final: 25%
Final grades will be assigned based on absolute percentage as follows:
| Absolute percentage | Grade |
|---|---|
| [ 100, 90 ] | 4.0 |
| ( 90, 85 ] | 3.5 |
| ( 85, 80 ] | 3.0 |
| ( 80, 75 ] | 2.5 |
| ( 75, 70 ] | 2.0 |
| ( 70, 60 ] | 1.0 |
| ( 60, 0 ] | 0.0 |
where [ ] denotes inclusion and ( ) denotes exclusion. The instructor reserves the right to move the thresholds down (but not up) based on the distribution of final percentages.
All homework must be done independently, or you will be penalized for plagiarism. The instructor and the TA will be carefully looking into your code.
Most homework contains a written component and a programming component. Therefore, most homework submission should include a report and some Python code. The report should be submitted in hard-copy BEFORE class on the due date. The code file should be submitted electronically on MSU D2L (one submission per student). Late penalty is 15% point deduction per day for the first three days, after which the submission will not be accepted. Exceptions/extensions can be given to students with documented and valid excuse. Students need to provide evidence for their excuse and must notify the instructor before the original due date. A student may request up to 2 excused extensions.
Most homework requires Python programming.
Students will be required to complete two in-class exams and one final exam. In-class exams will focus on topics taught since the last exam. The final exam will be comprehensive.
Students are required to attend all classes and actively participate in discussions.
Student leaders have recognized the challenging task of discouraging plagiarism from the academic community. The Associated Students of Michigan State University (ASMSU) is proud to be launching the Spartan Code of Honor academic pledge, focused on valuing academic integrity and honest work ethics at Michigan State University. The pledge reads as follows:
As a Spartan, I will strive to uphold values of the highest ethical standard. I will practice honesty in my work, foster honesty in my peers, and take pride in knowing that honor is worth more than grades. I will carry these values beyond my time as a student at Michigan State University, continuing the endeavor to build personal integrity in all that I do.
The Spartan Code of Honor academic pledge embodies the principles of integrity that every Spartan is required to uphold in their time as a student, and beyond. The academic pledge was crafted with inspiration of existing individual college honor codes, establishing an overarching statement for the entire university. It was formally adopted by ASMSU on March 3, 2016, endorsed by Academic Governance on March 22, 2016, and recognized by the Provost, President, and Board of Trustees on April 15, 2016. Student conduct that is inconsistent with the academic pledge is addressed through existing policies, regulations, and ordinances governing academic honesty and integrity: Integrity of Scholarship and Grades, Student Rights and Responsibilities, and General Student Regulations.
Sign Spartan Code of Honor here!
Article 2.3.3 of the Academic Freedom Report states that The student shares with the faculty the responsibility for maintaining the integrity of scholarship, grades, and professional standards. In addition, the (insert name of unit offering course) adheres to the policies on academic honesty as specified in General Student Regulations 1.0, Protection of Scholarship and Grades; the all University Policy on Integrity of Scholarship and Grades; and Ordinance 17.00, Examinations. (See Spartan Life: Student Handbook and Resource Guide and/or the MSU Web site: www.msu.edu.) Therefore, unless authorized by your instructor, you are expected to complete all course assignments, including homework, lab work, quizzes, tests and exams, without assistance from any source. You are expected to develop original work for this course; therefore, you may not submit course work you completed for another course to satisfy the requirements for this course. Also, you are not authorized to use the www.allmsu.com Web site to complete any course work in this course. Students who violate MSU academic integrity rules may receive a penalty grade, including a failing grade on the assignment or in the course. Contact your instructor if you are unsure about the appropriateness of your course work. (See also the Academic Integrity webpage.)
Essays, journals, and other materials submitted for this class are generally considered confidential pursuant to the Universitys student record policies. However, students should be aware that University employees, including instructors, may not be able to maintain confidentiality when it conflicts with their responsibility to report certain issues to protect the health and safety of MSU community members and others. As the instructor, I must report the following information to the Department of Police and Public Safety if you share it with me: Suspected child abuse/neglect, even if this maltreatment happened when you were a child, Allegations of sexual assault or sexual harassment when they involve MSU students, faculty, or staff, and Credible threats of harm to oneself or to others. These reports will trigger contact from the Department of Police and Public Safety who will want to talk with you about the incident that you have shared. In almost all cases, it will be your decision whether you wish to speak with that individual. If you would like to talk about these events in a more confidential setting you are encouraged to make an appointment with the MSU Counseling Center.
Michigan State University is committed to providing equal opportunity for participation in all programs, services and activities. Requests for accommodations by persons with disabilities may be made by contacting the Resource Center for Persons with Disabilities at 517-884-RCPD or on the web at rcpd.msu.edu. Once your eligibility for an accommodation has been determined, you will be issued a Verified Individual Services Accommodation (VISA) form. Please present this form to me at the start of the term and/or two weeks prior to the accommodation date (test, project, etc.). Requests received after this date may not be honored.
Article 2.III.B.4 of the Academic Freedom Report (AFR) for students at Michigan State University states: The student’s behavior in the classroom shall be conducive to the teaching and learning process for all concerned. Article 2.III.B.10 of the AFR states that The student has a right to scholarly relationships with faculty based on mutual trust and civility. General Student Regulation 5.02 states: No student shall ... interfere with the functions and services of the University (for example, but not limited to, classes ...) such that the function or service is obstructed or disrupted. Students whose conduct adversely affects the learning environment in this classroom may be subject to disciplinary action through the Student Judicial Affairs office.
Essays, journals, and other materials submitted for this class are generally considered confidential pursuant to the University's student record policies. However, students should be aware that University employees, including instructors, may not be able to maintain confidentiality when it conflicts with their responsibility to report certain issues to protect the health and safety of MSU community members and others. As the instructor, I must report the following information to other University offices (including the Department of Police and Public Safety) if you share it with me:
--Suspected child abuse/neglect, even if this maltreatment happened when you were a child,
--Allegations of sexual assault or sexual harassment when they involve MSU students, faculty, or staff, and
--Credible threats of harm to oneself or to others.
These reports may trigger contact from a campus official who will want to talk with you about the incident that you have shared. In almost all cases, it will be your decision whether you wish to speak with that individual. If you would like to talk about these events in a more confidential setting you are encouraged to make an appointment with the MSU Counseling Center.
The preparation of this course has benefited from CPTS 483 at Washington State University (Instructor: Dr. Shuiwang Ji), CSE 591 (Dr. Jieping Ye now in UMich) and CSE 575 (Dr. Hanghang Tong) at Arizona State University.