CSE512: Machine LearningSpring 2021, Time: Tues & Thurs 9.45am – 11.05am, Zoom Online Click here for Zoom link. You will need to sign in with your NetID Google Account to view. Homework 1. You will need to sign in with your NetID Google Account to view. This page provides some information to students who are considering to enroll in the course. Below are tentative grading scheme and syllabus, which are subject to change. Once you have enrolled in the course, you should go to Blackboard for updated information and futher annoucements. The content of this page will not be updated two weeks after the course has begun. This course covers fundamental machine learning concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., image classification, object detection) and medical data analysis (e.g., Covid-19 prediction) will motivate the coursework and material. This course is intended for graduate students who already have good programming skills and adequate background knowledge in mathematics, including probability, statistics, and linear algebra. This course is offered by the Department of Computer Science, and students from the department will have priority in registering for this course. If there are still spaces, students from AMS and BMI will be given special permission to enroll. Students from other departments won't be able to enroll in this course due to resource constraint. For special permission to enroll, attend the few two weeks of lectures (using the Zoom link provided in the Google doc above), and I will tell you how to enroll in the course at the end of the second week if: 1) you are still interested in taking the course, and 2) spaces are still available. GradingThere will be 5-6 homework assignments, two midterm exams, and several class quizzes.
Weights are approximate and subject to change. You are expected to do homework assignments by yourselves. Even if you discuss them with your classmates, you should turn in your own code and write-up. You can have one sheet of paper with notes in the exams. Tentative Syllabus
Students who are enrolled in the in-person/hybrid session of this course must attend at least two out of three in-person sessions, which are highlighted in the above table. Students who fail to do so will not receive a passing grade. In-person sessions are held during the regular class hours, in Room 120 of the New Computer Science building (NCS 120). You must physically be in NCS 120 during the in-person sessions, but you will be watching synchronous Zoom lectures on your own computers. Bring your own computers and headphones. TextbooksTextbooks are optional. Books that I love and recommend:
Other textbooks that may be more useful or accessible:
Student Accessibility Support Center StatementIf you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, 128 ECC Building, (631) 632-6748, or via e-mail at: sasc@stonybrook.edu. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential. Academic Integrity StatementEach student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology & Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at http://www.stonybrook.edu/commcms/academic_integrity/index.html Critical Incident ManagementStony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of University Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students’ ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook. Attendance and Covid-19Students are expected to attend every class, report for examinations and submit major graded coursework as scheduled. If a student is unable to attend lecture(s), report for any exams or complete major graded coursework as scheduled due to extenuating circumstances, the student must contact the instructor as soon as possible. Students may be requested to provide documentation to support their absence and/or may be referred to the Student Support Team for assistance. Students will be provided reasonable accommodations for missed exams, assignments or projects due to significant illness, tragedy or other personal emergencies. In the instance of missed lectures or labs, the student is responsible for insert course specific information here (examples include: review posted slides, review recorded lectures, seek notes from a classmate or identified class note taker, write lab report based on sample data). Please note, all students must follow Stony Brook, local, state and Centers for Disease Control and Prevention (CDC) guidelines to reduce the risk of transmission of COVID. For questions or more information click here. |