ECE/TCOM5583: Information Theory and Probabilistic ProgrammingThere has been a strong resurgence of AI in recent years. An important core technology of AI is statistical learning, which aims to automatically “program” machines with data. While the idea can date back to the 50's of the last century, the plethora of data and inexpensive computational power allow the techniques to thrive and penetrate into every aspect of our daily lives — customer behavior prediction, financial market prediction, fully automatic surveillance, selfdriving vehicles, autonomous robots, and beyond. Information theory was first introduced and developed by the great communications engineer, Claude Shannon in the 50's of the last century. The theory was introduced in an attempt to explain the principle behind pointtopoint communication and data storing. However, the technique has been incorporated into statistical learning and has inspire many of the underlying principles. In this graduate course, we would try to explore the exciting area of statistical learning from the perspectives of information theorists. It facilitates students to have a deeper understanding of the omnipresent field of statistical learning and to appreciate the widespread significance of information theory. Moreover, we will look into recent advance in probabilistic programming technology that facilitates users to tackle inference problems through computer programs. The course will start by providing an overview of information theory and statistical learning. We will then aid students to establish a solid foundation on core information theory principles including information measures, AEP, source and channel coding theory. We will then introduce common and yet powerful statistical techniques such as Bayesian learning, decision forests, and belief propagation algorithms and discuss how these modern statistical learning techniques are connected to information theory. To summarize, we will skim through some probablistic programming tools. The main reference text is a book by Professor Mackay — Information Theory, Inference, and Learning Algorithms but we will also borrow heavily from materials available online. Other most important reference texts are
Other Reference Materials
Office HoursThere are no “regular” office hours. But you are welcome to come catch me anytime or contact me through Discord. Course Syllabus (Tentative)
N.B. You will expect to expose to some Python and Matlab. You won't become an expert on these things after this class. But it is good to get your hands dirty and play with them early. Adjustments for Pregnancy/Childbirth Related IssuesShould you need modifications or adjustments to your course requirements because of documented pregnancyrelated or childbirthrelated issues, please contact me as soon as possible to discuss. Generally, modifications will be made where medically necessary and similar in scope to accommodations based on temporary disability. Please see www.ou.edu/content/eoo/faqs/pregnancyfaqs.html for commonly asked questions. Title IX ResourcesFor any concerns regarding genderbased discrimination, sexual harassment, sexual misconduct, stalking, or intimate partner violence, the University offers a variety of resources, including advocates oncall 24.7, counseling services, mutual no contact orders, scheduling adjustments and disciplinary sanctions against the perpetrator. Please contact the Sexual Misconduct Office 4053252215 (85, MF) or OU Advocates 4056150013 (24.7) to learn more or to report an incident. ProjectsFinal project report is due on 12/15. Late Policy
GradingQuizzes (In class participation): 10% (extra credits). Presentations: 20%. Homework: 20%. "Midterm": 20%. take home but will only have half of a day to complete. Final Project: 40%. Final Grade:
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