Spring 2018 CISC5800: Machine Learning
Click for Class Schedule
General Information
| Instructor: | Dr. Yijun Zhao |
| Email: | yzhao11@fordham.edu |
| Lectures: | Thursday 5:30pm - 7:45pm, LL311 |
| Office Hours: | Thursday 4:30pm - 5:30pm, LL610E |
| TA Office Hours: | Wednesday 2:00pm - 4:00pm, LL612 |
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| Other Resource: | Piazza signup link: click here |
Textbook
Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin, AMLBook (2012)
Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer (2011)
Note: Both books are on reserve for CS5800 at Quinn Library (Lincoln Center) with 3 hours loans.
Additional readings will be distributed through out the course.
Description of Course
This course gives an overview of start of the art concepts,
techniques, and algorithms in machine learning, beginning with
topics such as linear regression and classification, moving on to more
recent topics such as deep learning, convolutional neural networks and hidden Markov models,
ending with modern learning theory. The course will give the student basic
ideas and intuition behind modern machine learning methods as well as a bit more
formal understanding of how, why, and when they work.
Prerequisites
The students are expected to:
- Have knowledge in data structures, algorithms, basic linear algebra, and basic statistics.
- Familiar with Python programming language.
Homework
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There will be four HW assignments.
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Each assignment's due date is posted on the schedule.
- Each assignment is graded with a scale of 100.
Course Project
There will be a course project to predict whether a person will be readmitted
within 30 days after being discharged from the hospital. The dataset you will be working on
is a real world dataset, representing 10 years (1999-2008) of clinical care at 130 US hospitals
and integrated delivery networks. The project can be completed either individually or as a group (strongly recommended). A group can have at most 5 people.
More details will be discussed in class.
Exams
- There will be one midterm exam.
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There will be a final exam.
The final exam is cumulative with emphasis on the material covered after midterm.
Grading
- Course projects: 20%
- Homework: 40%
- Midterm exam: 20%
- Final exam: 20%
Note:
- Failing to complete a HW or the course project on time
will cause a 10% reduction for each additional day.
- Dispute on grading must be resolved within two weeks after receiving your score.
Additional Remarks
- Academic Honesty
All work produced in this course should be your own unless it is specifically stated that you may work with others.
You may discuss the homework problems with other students generally, but may not provide complete solutions
to one another; copying of homework solutions is always unacceptable and will be considered a violation of
Fordham's academic integrity policy. Violations of this policy will be handled in accordance with university
policy which can include automatic failure of the assignment and/or failure of the course.
For more information, please refer to the Academic Integrity website.
- Makeup Exam
There will be no make-up exams given after the exam date. If you know in advance that you will have to miss an exam, you must check with me (in advance) to avoid getting a zero for that exam. In case of illness on an exam date, please contact me as soon as possible, so that appropriate arrangements can be made.
Last modified: Jan. 10, 2018