Models and Technologies for Information Services


c_weekno Title Body
1 Course overview/Paper assignment Mathematical foundations
10 Latent Semantic Analysis Cluster-Based FAQ Retrieval Using Latent Term Weights Latent Semantic Models for Collaborative Filtering
11 Term Project Proposals
12 (Near) Final Exam Mining Correlated Bursty Topic Patterns from Coordinated Text Streams Tracking Dynamics of Topic Trends Using a Finite Mixture Model
13 Applications of Statistical Learning Methods in Practice
14 Applications of Text Mining Techniques in Practice
15 Final Term Project Presentations & Demo
2 Mathematical foundations A vector space approach to tag cloud similarity ranking Online Video Recommendation through Tag-Cloud Aggregation
3 SimRank: A Measure of Structural-Context Similarity Automatic Generation of Overview Timelines Hidden Markov Models
4 Hidden Markov Models Learning Hidden Markov Model Structure for Information Extraction Credit Card Fraud Detection Using Hidden Markov Model
5 Fast Inference and Learning in Large-State-Space HMMs A Hidden Markov Model of Customer Relationship Dynamics VOGUE: A Variable Order Hidden Markov Model with Duration Based on Fre-quent Sequence Mining Bursty and Hierarchical Structure in Streams
6 Linear Discrimination and SVMs
7 Searching Social Media Streams on the Web Finding Advertising Keywords on Web Pages Optimizing Search Engines using Clickthrough Data Hierarchical Document Categorization with Support Vector Machines
8 Predicting Structured Objects with Support Vector Machines Hidden Markov Support Vector Machines Latent Dirichlet Allocation
9 Dynamic Topic Models Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends Continuous Time Dynamic Topic Models Characterizing Microblogs with Topic Models