• Day: Tuesday
• Time: 9:30 am - 11:00 am
• Venue: Room 1027, HSB Engineering Building

• Day: Monday
• Time: 2:00 pm - 3:30 pm
• Venue: Room 1021, HSB Engineering Building

• Day: Tuesday
• Time: 2:00 pm - 4:00 pm
• Venue: Room 1021, HSB Engineering Building

• Day: Monday
• Time: 1:30 pm - 3:30 pm
• Venue: Room 1027, HSB Engineering Building

• Day: Tuesday
• Time: 2:00 pm - 4:00 pm
• Venue: Room 1027, HSB Engineering Building

Reference:

http://www.deeplearningbook.org/

http://www.deeplearningbook.org/lecture_slides.html

https://github.com/HFTrader/DeepLearningBook

[ 2013t1 | 2012t1 | 2012t0 | 2011t2 | 2011t1 | 2010t2 | 2010t1 | 2009t2 | 2009t1 | 2008t2 ]

• Day: Monday
• Time: 10:30 am - 12:30 pm
• Venue: Room 1021, HSB Engineering Building

• Day: Monday
• Time: 3:30 pm - 5:00 pm
• Venue: Room 1021, HSB Engineering Building

• Day: Tuesday
• Time: 2:30 pm - 4:00 pm
• Venue: Room 1021, HSH Engineering Building

• Day: Monday
• Time: 2:30 pm - 3:30 pm
• Venue: Room 1021, HSH Engineering Building

• Day: Tuesday
• Time: 1:30 pm - 2:30 pm
• Venue: Room 1022, HSH Engineering Building

• Day: Wednesday
• Time: 3:30 pm - 4:30 pm
• Venue: Room 1022, HSH Engineering Building

• Day: Tuesday
• Time: 2:00 pm - 3:30 pm
• Venue: Room 1022, HSH Engineering Building

• Day: Monday
• Time: 2:00 pm - 3:30 pm
• Venue: Room 1027, HSH Engineering Building

• Day: Monday
• Time: 1:00 pm - 2:30 pm
• Venue: Room 1022, HSH Engineering Building

• Wikipedia
• Challenges and Opportunities with Big Data - A community white paper

• Large-Scale Machine Learning and Big Data
• Open Course on Coursera: Web Intelligence and Big Data (You may need to register)

• NIPS2012 Big Learning
• MMDS 2012. Workshop on Algorithms for Modern Massive Data Sets
• UC Berkeley AMP Big Data Bootcamp
• IEEE BigData Congress 2013
• IEEE International Conference on Big Data 2013

• Big Data@CSAIL, MIT, 23 nodes (projects), e.g., linked data, computer vision, natural language, machine learning, social, etc.
• NASA tournament lab, big data challenge, Apply the process of open innovation to conceptualizing new and novel approaches to using “big data” information sets from various U.S. government agencies, e.g., health, energy and earth science.
• Fastlab, Georgia Tech, make all textbook state-of-the-art machine learning methods computationally tractable on big datasets.
• Noah'S ark lab, HK, their goal is to push the frontier of our understanding on how knowledge can be learned and intelligence can be realized from the Big Data.
• Select Lab, CMU, the techniques they develop encompass a wide range of topics, including probabilistic graphical models, active learning and value of information, distributed algorithms, probabilistic inference, decision making under uncertainty, online learning and game theory.

• Jimmy Lin (University of Maryland)
• Ron Bekkerman (LinkedIn)
• Misha Bilenko (MSR)
• John Langford (Yahoo! Research)
• Alexander Gray (GIT)
• Carlos Guestrin (previously in CMU, now in UW)

• DATA.GOV, Data portal provided by US government
• Blog data, 3TB of Spinn3r dataset
• Twitter Data, Twitter follow links data
• eHealth Initiative, Data and analytics resources

• Scaling Up Machine Learning-Parallel and Distributed Approaches, State-of-the-art platforms and algorithm choices, Hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters), Programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ), Learning settings (e.g., semi-supervised and online learning), Example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., speech recognition and object recognition in vision)
• Faster Learning for Massive Datasets by Alex Gray, The presentation describes new approaches for online learning and stochastic programming, which achieve both tighter theoretical bounds across the board and significant empirical gains over state-of-the-art approaches including stochastic gradient descent and mirror descent. It also presents a scheme for distributed online learning exhibiting first-of-a-kind theoretical and empirical gains. For nonlinear kernelized methods, kernel matrix multiplications and summations become a bottleneck. It shows fast algorithms which provably reduce computation times from quadratic to linear time, with corresponding empirical runtime results, demonstrated on over 10,000 cores.
• Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling, In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will then pay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
• microsoft big data

• News and events about Big Data
• News and research papers about Big Data
• IBM Big Data Hub: Understanding big data for the enterprise
• Big Data for Health Care

• IBM Watson deep Q&A, evidence-based decision support, deep question answering, Jeopardy!, Health care
• Needleseek, mine open-domain semantic knowledge from web-scale data sources and answer user requests based on the mined semantic knowledge

• Fast Data in the Era of Big Data: Twitter’s Real-Time Related Query Suggestion Architecture
• Fast Candidate Generation for Two-Phase Document Ranking: Postings List Intersection with Bloom Filters
• Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling
• Large-Scale Machine Learning at Twitter
• Large-scale matrix factorization with distributed stochastic gradient descent
• SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
• Large-scale machine learning with stochastic gradient descent
• Routing Questions to the Right Users in Online Communities
• Carlos Guestrin's work since 2009

• Data-Intensive Text Processing with MapReduce
• Scaling up Machine Learning: Parallel and Distributed Approaches
• Mining of Massive Datasets
• Hadoop: The Definitive Guide
• Big Data Glossary