Instructor : Dr. Sreyasee Das Bhattacharjee
Email : sreyasee@buffalo.edu
Office: Davis Hall 349
Office Hours: T, R, 200pm to 315pm and By Appointment
General Information
Lectures: T, R, 11:00 AM - 12:20 PM
Room: Park 250
TA(s):TBD
Course Overview: Pattern recognition handles the problem of identifying object characteristics and categorizing them, given its noisy representations using computer algorithms and pattern visualization. While pattern recognition, machine learning and data mining are all about learning to label objects, pattern recognition researchers are interested in learning the intrinsic signal patterns and ways to visualize them. Pattern recognition workflow involves iterating between data acquisition, preprocessing, feature extraction, feature selection, model selection, training, and evaluation. While traditional pattern recognition mostly concerns feature selection and model training, the availability of big data and neural network frameworks such as Tensorflow and Torch also makes automatic feature extraction as a pattern recognition topic. In this introductory course, we will be covering the concepts of traditional and modern pattern recognition, probabilistic methods to offer AI based solutions to several real-world problems, and prepare students for advanced research/industrial projects in this field.
Piazza : We will use Piazza to answer questions and post announcements about the course. Please sign up here.
Syllabus : You can access the Syllabus here.
Grade Composition : 5 Programming Assignments, 10% each
~ 4 Written Assignments, 20%
Mid-term(s): 10%
Final: 20%
Class Activities/Self-assesments: 5% of total (Extra Credit!)
Textbook/Refrence Books:
Pattern Classification, 2nd Edition, by Richard O. Duda, Peter E . Hart, and David G. Stork (DHS)
Pattern Recognition and Machine Learning, by Chris Bishop (2006).
Other Supporting Materials (will continue to be extended later on):
Data Clustering: 50 Years Beyond K-Means, Pattern Recognition Letters, Vol. 31, No. 8, pp. 651-666, June 2010, by A. K. Jain
Algorithms for Clustering Data, Prentice-Hall, 1988, by A. K. Jain, R. C. Dubes
Deep learning with Python, by Francois Chollet
Deep learning, by Ian Goodfellow
Building Probabilistic Graphical Models with Python, by Kiran R Karkera
Introduction to support vector machines and other kernel-based learning methods, by Cristianini and Shawe-Taylor
Brief Introduction to Graphical Models and Bayesian Networks, by Kevin Murphy.
Course Schedule
(Tentative, more Reading Materials to be added during the course)| Week | Lecture | Reading Materials |
|---|---|---|
| Week 1 | Introduction, Bayesian Decision Theory | DHS, Chapter 1 |
| Week 2 | Maximum Likelihood, Bayesian Parameter Estimation | DHS, Chapter 2.1-2.10 |
| Week 3, 4 | Parameter Estimation, Component Analysis, and Discriminents | DHS, Chapter 3.1-3.8 |
| Week 4, 5 | Non-Parametric Techniques | DHS, Chapter 4.1-4.6 |
| Week 6 | Linear Discriminant Functions | DHS, Chapter 5.1-5.9 |
| Week 7 | Support Vector Machines and Kernel Methods | DHS, Chapter 5.11 |
| Week 8 | MIDTERM | |
| Week 9,10 | Stochastic Methods, Graphical Models | DHS, Chapter 7.1-7.4 |
| Week 11 | Neural Networks | |
| Week 12 | Deep Learning | |
| Week 13 | Unsupervised Learning and Clustering | |
| Week 14 | Time Series |
Academic Integrity:
(Short) Do not cheat! You will be caught and punished. Our department is serious about graduating ethical and upstanding computer scientists. The policy has recently been updated and will be
enforced.
(Long) All academic work must be your own. Plagiarism, defined as copying or receiving materials
from a source or sources and submitting this material as one's own without acknowledging the
particular debts to the source (quotations, paraphrases, basic ideas), or otherwise representing the
work of another as one’s own, is never allowed. Collaboration, usually evidenced by unjustifiable
similarity, is never permitted in individual assignments. Any submitted academic work may be
subject to screening by software programs designed to detect evidence of plagiarism or collaboration. Also, do not post any of the course material outside of the Course piazza page. It will be
interpreted as an attempt to get non-approved help. For more info :
UB CSE Academic Integrity
Working with others: Please do help each other! This material is fun, but can be challenging. Discussing it with peers can deepen your understanding. You can talk about the homework problems and ways of approaching them, however, every person must write up solutions and code separately. We will compare all submissions with each other AND non-approved sources. I you can find something online, so can we.
Special Accommodations: In case of need of special accommodations please go the following link for more information.
Special Accommodations.