- Instructor: Prof.
Sung-Hyuk Cha
- Email: scha@pace.edu
- Office: Office: 15 Beekman st. rm 1119
-
Office Hours:
- Tuesday 4:10pm ~ 5:50pm
- Thursday 4:10pm ~ 5:50pm
- Friday 4:10pm ~ 5:50pm
- CRN: 72871/72872
- Meeting:
- Time: Thursday 6:10~9:00 P.M., Fall 2023
- Place:
15 BK 1111
- Textbook:
Handouts (lecture slides and related articles) will be distributed on the
http://classes.pace.edu.
Recommended book:
- Description:
This course focuses on the fundamental concepts, theories, and algorithms for pattern recognition and machine learning.
Diverse application areas such as optical character recognition, speech recognition, and biometrics are discussed.
Topics covered include supervised and unsupervised (clustering) pattern classification algorithms, parametric and non-parametric supervised learning techniques, including Bayesian decision theory, discriminant functions, the nearest neighbor algorithm, and neural networks.
- Learning Outcomes and Major Topics:
- understand, explain, and compare supervised versus unsupervised machine learning algorithms;
- understand, explain, and compare the two main types of supervised machine learning algorithms, parametric and non-parametric;
- understand, explain, and discuss various application areas of machine learning;
- understand, explain, and discuss a variety of popular machine learning algorithms, including neural networks, support vector machines, deep networks and convolutional layers, nearest neighbor, and decision trees;
- explain and discuss the role of machine learning and machine learning systems in the area of biometrics;
- design and implement a machine learning system for a specific application problem.
- Schedule: (tentative)
| Week |
Topics |
| 1 (9/7) |
Decision tree and entropy |
| 2 (9/14) |
Hierarchical Clustering |
| 3 (9/21) |
Ensemble methods |
| 4 (9/28) |
Factor analysis |
| 5 (10/5) |
Regression |
| 6 (10/12) |
Linear Classifiers |
| 7 (10/19) |
Artificial Neural Networks |
| 8 (10/26) |
Research proposal due
|
9 (11/2) |
Genetic Algorithms |
| 10 (11/9) |
Self Organizing Map (SOM) |
| 11 (11/16) |
Tree based Regression |
| 12 (11/23) |
Thanksgiving week |
| 13 (11/30) |
Final Presentation |
| 14 (12/7) |
Review |
| 15 (12/14) |
Final Exam |
- Evaluation:
- Attendance & participation (20%):
- Homework (20%):
- Research Project (30%):
- progress report and presentation (10%)
- final presentation (10%)
- final report (10%)
- Final exam (30%)
- Accommodations for Students with Disabilities
The University's commitment to equal educational opportunities for students with disabilities includes providing reasonable accommodations for the needs of students with disabilities. To request a reasonable accommodation for a qualified disability a student with a disability must self-identify and register with the Office of Disability Services for his or her campus. No one, including faculty, is authorized to evaluate the need for or grant a request for an accommodation except the Office of Disability Services. Moreover, no one, including faculty, is authorized to contact the Office of Disability Services on behalf of a student. For further information, please see Resources for Students with Disabilities at www.pace.edu/counseling/resources-and-support-services-for-students-with-disabilities.
- Academic Integrity: (From the Student Handbook) Students are required to be honest and ethical in satisfying their academic assignments and requirements. Academic integrity requires that, except as may be authorized by the instructor, a student must demonstrate independent intellectual and academic achievements. Therefore, when a student uses or relies upon an idea or material obtained from another source, proper credit or attribution must be given. A failure to give credit or attribution to ideas or material obtained from an outside source is plagiarism. Plagiarism is strictly forbidden. Every student is responsible for giving the proper credit or attribution for any quotation, idea, data, or other material obtained from another source that is presented (whether orally or in writing) in the student’s papers, reports, submissions, examinations, presentations and the like. Individual schools and programs may have adopted additional standards of academic integrity. Therefore, students are responsible for familiarizing themselves with the academic integrity policies of the University as well as of the individual schools and programs in which they are enrolled. A student who fails to comply with the standards of academic integrity is subject to disciplinary actions such as, but not limited to, a reduction in the grade for the assignment or the course, a failing grade in the assignment or the course, suspension and/or dismissal from the University.
|