In addition to the challenges we face in acquiring, storing, and transmitting very large amounts of data, we also frequently desire to “learn” from the data in a number of senses. This arises in many important and emerging signal processing problems when we lack a priori analytical models. In this case we must learn data models and tune processing algorithms based entirely on training data. Example applications include:
search engines, medical diagnosis, detecting credit card fraud, stock market analysis, speech and handwriting recognition, object recognition in computer vision, spam filtering.
We are exploring a wide range of machine learning algorithms to that aid in tasks including data visualization and exploration, dimensionality reduction, nonlinear regression, and pattern classification.
Courses
CEVE 496 SYSTEM I.D. & MACHINE LEARNING
CEVE 596 SYSTEM I.D. & MACHINE LEARNING
COMP 441 LARGE-SCALE MACHINE LEARNING
COMP 502 NEURAL MACHINE LEARNING I
COMP 540 STATISTICAL MACHINE LEARNING
COMP 542 LARGE-SCALE MACHINE LEARNING
COMP 602 NEURAL MACHINE LEARNING II
COMP 640 GR SEM IN MACHINE LEARNING
COMP 642 MACHINE LEARNING
DSCI 303 MACHINE LEARNING FOR DS
ELEC 478 INTRO TO MACHINE LEARNING
ELEC 502 NEURAL MACHINE LEARNING I
ELEC 515 EMBEDDED MACHINE LEARNING
ELEC 578 INTRO TO MACHINE LEARNING
ELEC 602 NEURAL MACHINE LEARNING II
ELEC 681 FUNDAMENTALS MACHINE LEARNING
STAT 413 INTRO TO STAT MACHINE LEARNING
STAT 502 NEURAL MACHINE LEARNING I
STAT 602 NEURAL MACHINE LEARNING II
STAT 613 STAT MACHINE LEARNING
Centers and Institutions
Research Groups
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- Yakobson Research Group (Theory and modeling of structure, kinetics, and properties of materials derived from macroscopic and fundamental molecular interactions)
- Mesoscale Materials Modeling Group
- Rice Engineering Laboratory for Advanced Computational Science
- Computer-aided Programming @ Rice
- Kavraki Lab (Computational robotics and computational medicine)
- Robotics and Intelligent Systems Lab
- Nagarajaiah Research Group (Structural dynamic systems, earthquake engineering, advanced seismic protection, smart structures, adaptive stiffness structures, sparse structural system identification, and strain sensing using nano-materials)
- Duenas-Osorio Research Group (Computational and theoretical models for structure and infrastructure system reliability, resilience and risk assessment)
- Padgett Research Group (Application of probabilistic methods for risk assessment of structures, including the quantification and promotion of infrastructure sustainability
- Bayesian Statistics
- Statistical Learning
- Ajayan Research Group (Advanced nanomaterials with specific application areas in alternative energy, multifunctional nanocomposites, and electronics/sensor technologies)
- Rice Computational Neuromechanics Lab
- Particle Flow and Tribology Lab
- Mechatronics and Haptic Interfaces Lab
- Digital Signal Processing Group
- Rice Efficient Computing Group (Efficient technologies for future computing, communication and interfacing)
- Intelligent Software Systems Laboratory
- Big Data and Optical Lightpaths Driven Lab
- Rushlab - design and implement exponentially resource-frugal and scalable machine learning (ML) algorithms
- Sec Lab - web and smartphones, peer-to-peer networking, electronic voting systems
- The Automated Reasoning Group
- Computational Robotics & Biomedicine Lab