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Welcome to the webpage for Prof. Alexander Ihler's Statistical Learning and Inference group at UCI. We are interested in using probabilistic models to represent and understand real-world phenomena, using approximation algorithms to estimate probabilistic quantities efficiently, and learning probabilistic models from data. We focus primarily on algorithms for learning and estimation in graphical models. With applications to sensor networks. Image processing and computer vision, and computational biology.

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Welcome to the webpage for Prof. Alexander Ihler's Statistical Learning and Inference group at UCI. We are interested in using probabilistic models to represent and understand real-world phenomena, using approximation algorithms to estimate probabilistic quantities efficiently, and learning probabilistic models from data. We focus primarily on algorithms for learning and estimation in graphical models. With applications to sensor networks. Image processing and computer vision, and computational biology.

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9 teaching and classes

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SLI | Main / HomePage | sli.ics.uci.edu Reviews

https://sli.ics.uci.edu

Welcome to the webpage for Prof. Alexander Ihler's Statistical Learning and Inference group at UCI. We are interested in using probabilistic models to represent and understand real-world phenomena, using approximation algorithms to estimate probabilistic quantities efficiently, and learning probabilistic models from data. We focus primarily on algorithms for learning and estimation in graphical models. With applications to sensor networks. Image processing and computer vision, and computational biology.

INTERNAL PAGES

sli.ics.uci.edu

1

SLI | Projects / GraphicalModels

http://sli.ics.uci.edu/Projects/GraphicalModels

Graphical models are used to encode structured relationships among collections of random variables. These relationships may be logical (for example, expressing forbidden combinations of values), but most often are probabilistic, expressing the relative likelihood of co-occurrence. These relationships are expressed as a graph in which directly related variables are connected, which can then be used to simplify and automate reasoning over the full system. Understanding and extending variants of the popular.

2

SLI | Classes / Classes

http://sli.ics.uci.edu/Classes/Classes

Learning in Graphical Models (graduate). Machine Learning and Data Mining (undergrad). Introduction to Graphical Models (undergrad). Machine Learning and Data Mining (undergrad). Intro to Machine Learning (graduate). Learning in Graphical Models (graduate). Machine Learning and Data Mining (undergrad). Intro to Machine Learning (graduate). Collaborative Filtering undergraduate summer research. Recommender Systems and Collaborative Filtering (undergraduate). Machine Learning and Data Mining (undergraduate).

3

SLI | Projects / DataMining

http://sli.ics.uci.edu/Projects/DataMining

Data mining describes the task of organizing and searching massive quantities of data for patterns of interest, for example summarizing large subsets of the data (clustering), finding unusual or unexpected patterns (anomaly detection), or constructing new, more meaningful representations of the data (latent space representations). Our group uses probabilistic models to perform these tasks - graphical models are learned from the data to represent and assess expected behavior. Mining sensors and count data.

4

SLI | Projects / Sensor Networks

http://sli.ics.uci.edu/Projects/Sensor

Improvements in sensing technology and wireless communications have greatly enhanced our ability to view and understand the world we live in. Collaborative networks of sensors can be deployed for relatively little cost, gathering and reporting real-time data about their locale. I am interested in the estimation and information processing problems which arise in sensor networks. Sensor localization and message-passing algorithms. An important first step in many sensor deployments is to. For particle-based...

5

SLI | Group / Group

http://sli.ics.uci.edu/Group/Group

BH 4066, x4-3645. PhD, Fall 2015. Thesis, " Approximate Inference in Graphical Models. PhD, Spring 2015. Thesis, " Probabilistic Models for Brain Image Collection, Classification and Functional Connectivity. PhD, Fall 2014. Thesis, " Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. PhD, Spring 2014. Thesis, " Bottom-Up Approaches to Approximate Inference and Learning in Discrete Graphical Models. PhD, Spring 2013. PhD, U Chicago, 2011 (unofficial advisor). PhD, Fall 2010.

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SLI | Main / HomePage

Welcome to the webpage for Prof. Alexander Ihler's Statistical Learning and Inference group at UCI. We are interested in using probabilistic models to represent and understand real-world phenomena, using approximation algorithms to estimate probabilistic quantities efficiently, and learning probabilistic models from data. We focus primarily on algorithms for learning and estimation in graphical models. With applications to sensor networks. Image processing and computer vision, and computational biology.

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