CMPE 547 - Bayesian Statistics and Machine LearningCatalog DataMachine learning approaches using Bayesian statistics. Graphical models, directed and undirected models, learning and inference, Hidden Markov Models (HMM's), Linear Dynamical Systems, message passing algorithms, Junction Tree, factor graphs, sum-product, hierarchical Bayesian modeling, Expectation-Maximisation, Variational Approximation techniques Course DescriptionThe grand challenge facing computer science in the 21st century is the processing and analysis
of large data. With the advance of sensor and storage technologies, and with the cost of data
acquisition dropping significantly, we are able to monitor complex systems over time, easily collect and record vast amounts of raw data. The main challenge is to extract meaningful information from these highly structured multivariate datasets that can be of interest for scientific, financial, political or technological purposes. In this course, we will cover computational methodologies for modeling and inference from a Bayesian perspective. In the Bayesian paradigm, data is viewed as realizations from highly structured probabilistic models. Once a model
is constructed, several interesting problems such as feature extraction, pattern recognition, retrieval, sensor fusion, coding, network analysis, classification, restoration, tracking, source separation or model selection can be formulated as Bayesian inference problems. In this context, graphical models provide a “language” to construct
models for quantification of prior knowledge. Unknown parameters in
this specification are estimated by probabilistic inference. Often,
however, the problem size poses an important challenge and in order to
render the approach feasible, specialized inference methods need to be
tailored to improve the computational speed and efficiency. The scope of this course is to review the fundamentals of probabilistic models, inference algorithms and associated data structures. We will review directed (Bayesian Networks) and undirected (Markov Random fields), factor graphs and junction trees. In particular, we will review exact inference, approximate deterministic (variational) inference techniques and Monte Carlo methods. Stochastic inference techniques are treated in more depth in a different course focusing entirely on Monte Carlo computation (offered in Bogazici as CMPE58N). Our ultimate aim of this course is to provide a basic understanding of probabilistic modeling for
machine learning, associated computational techniques such that the research students
can orient themselves in the relevant literature and
understand the current state of the art. Topics
Who could take this courseThis course teaches statistical techniques for modeling real world phenomena and dealing with uncertainty for making sense of data. As analysis of data is central in several application domains, techniques covered in this class have a quite wide applicability. Whilst our coverage is not exhaustive, in the past, students from several disciplines with a broad range of interests have benefited from the material
Graduate students and interested senior undergraduates are welcome to take the course with or without credit. You are welcome to sit in if space permits; just let me know with an email. Textbook
Reference TextbooksChapters from the following books:
Prerequisite or consent of instructorCmpE 343 (Introductory Probability and Statistics) or equivalent AdministrativeGrading
Total Credits3 Past Evaluation Comments from StudentsI am always quite surprised after course evaluations to see the wide range of reviews. Although I want to think of myself as a reasonably OK teacher, I typically get evaluation results with a high variance. The following comments left anonymously may be useful in choosing taking this course. Fall 2015
In brief, I am so happy with taking his lecture which is I think the most beneficial course for all of the students. In addition, he is not my advisor or anything has beneficial feedback for me he just does his bets I think and I would like to say thanks for having the lecturer as a member of the BOUN. Fall 2015
The instructor does not pay attention to students’ level of comprehension. The students are supposed to know a lot of things. The objective of the course content is unclear. The design, the proofs of the course content should be more explicit. I think the instructor does not have time or patience to explain a topic in detail. Also TAs are not very good at course content. Some explanations are misleading or incomplete. Fall 2015
I HOPE SOMEONE READS THIS! The course has no structure at all. The instructor just gives us a bunch of information. At the end of lectures, no one is able to answer the wuestion of what have I learned from this lecture. He mentions about many things but there is no structure in it. We first learn a topic and jump to another thing, come back to where we were… The instructor doesn't talk about the titles nor does he summarize what we learn. When he enters the class, he immediately starts writing complex equations without a detailed mentioning of what we will do, what we try to achieve etc. Eventhough I attend ALL the courses, I can't even tell what the subjects of the course were. I have to study everything on my own at home. Fall 2014
The course structure and subject material are remarkable. The only problems are delay in grading and announcement of grades and students’ poor understanding of probability which is not due to this course. Fall 2014
The course tries to cover a lot more than that is possible. There was not enough direction about how to brush up for required material like matrix algebra,calculus etc. A more balanced approach including writing code to implement the material learned would make the subject stick more. Too much abstraction and fast pace diminished the actual understanding of the material. Fall 2014
Assistants may get more and more involved. Also, sometimes I got lost in derivations and forgot what we are doing. Objectives might be more clear. Fall 2014
The solutions to assignments are inadequate. Most of them are not solved in problem sessions and the questions about them are not regarded as valuable. I believe that this lecture as a grad lecture should provide more insight for the begginers of computer enginering. Here are a few points to keep in mind:
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