CS 8803 DML: Data Management and Machine Learning (Fall 2018)

Date	Topic	Content	Presenter
08/21	Introduction	Course Introduction and Logistics Introduction to Part 1: Data Cleaning and ML	Xu Chu
08/23	Introduction	Introduction to Part 2: Data Exploration and ML Introduction to Part 3: Systems and ML Discussions of sample course projects	Xu Chu
Optional reading	Introduction	Data Management Challenges in Production Machine Learning Data Management in Machine Learning: Challenges, Techniques, and Systems	N.A.
08/28	N.A.	No Class (Instructor at VLDB)	N.A.
08/30	N.A.	No Class (Instructor at VLDB)	N.A.
Part I: Data Cleaning and ML
09/04	ML for Data Deduplication (1)	Introduction to papers in this class Interactive Deduplication using Active Learning On active learning of record matching packages.	Xu Chu Xiang Cheng Alex Mueller
09/06	ML for Data Deduplication (2)	Introduction to papers in this class Distributed Representations of Tuples for Entity Resolution Deep Learning for Entity Matching: A Design Space Exploration	Xu Chu Yuhong Wang Omar Sharifali
09/11	ML for Data Deduplication (3)	Introduction to papers in this class CrowdER: crowdsourced entity resolution Distributed Data Deduplication	Xu Chu Alex Mueller Thibaut Boissin
Optional reading	ML for Data Deduplication (4)	Duplicate Record Detection: A Survey	N.A.
09/13	ML for Data Cleaning (1)	Introduction to papers in this class Detecting Data Errors: Where are we and what needs to be done? HoloClean: Holistic Data Repairs with Probabilistic Inference	Xu Chu Florina Dutt Zhuoran Yu
09/18	ML for Data Cleaning (2)	Introduction to papers in this class Tracing Data Errors with View-Conditioned Causality∗ Data X-Ray: A Diagnostic Tool for Data Errors	Xu Chu Thibaut Boissin Saurabh Sawlani
Optional reading	ML for Data Cleaning (3)	Data Cleaning is a ML Problem that Needs Data Systems Help	N.A.
09/20	Data Cleaning for ML (1)	Introduction to papers in this class A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data ActiveClean: Interactive Data Cleaning For Statistical Modeling	Xu Chu Sanya Chaba Peng Li
09/25	Data Cleaning for ML (2)	Introduction to papers in this class Cleaning Crowdsourced Labels Using Oracles For Supervised Learning BoostClean: Automated Error Detection and Repair for Machine Learning	Xu Chu Jennifer Blase Xinran Shi
Optional reading	Data Cleaning for ML (3)	Impacts of Dirty Data: an Experimental Evaluation	N.A.
09/27	Data Wrangling/Transformation	Introduction to papers in this class Potter’s Wheel: An Interactive Data Cleaning System Transform-Data-by-Example (TDE): Extensible Data Transformation using Functions	Xu Chu Matthew Britton Prashanth Dintyala
10/02	Training Data Enrichment	Introduction to papers in this class Snorkel: Rapid Training Data Creation with Weak Supervision Combining Labeled and Unlabeled Data with Co-Training	Xu Chu Pranshu Trivedi Peng Li
10/04	Boosting	Introduction to papers in this class Multi-class AdaBoost XGBoost: A Scalable Tree Boosting System	Xu Chu Jayant Prakash Zhanhao Liu
Part 2: Data Exploration and ML
10/09	N.A.	No Class (Fall Recess)	N.A.
10/11	Relational Data Profiling (1)	Introduction to papers in this class TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies FastFDs: A Heuristic-Driven, Depth-First Algorithm for Mining Functional Dependencies from Relation Instances	Xu Chu Zhanhao Liu Nilaksh Das
10/16	Relational Data Profiling (2)	Introduction to papers in this class Discovering Denial Constraints Efficient Denial Constraint Discovery with Hydra	Xu Chu Yuhong Wang Xinran Shi
Optional reading	Relational Data Profiling (3)	Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms Profiling Relational Data – A Survey	N.A.
10/18	Model Interpretation (1)	Introduction to papers in this class “Why Should I Trust You?” Explaining the Predictions of Any Classifier Anchors: High-Precision Model-Agnostic Explanations	Xu Chu Yafei Zhang Saurabh Sawlani
10/23	Model Interpretation (2)	Introduction to papers in this class A Unified Approach to Interpreting Model Predictions Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation.	Xu Chu Yue Hu Yafei Zhang
Optional reading	Model Interpretation (3)	Interpretable ML Symposium Interpretable ML by H2O	N.A.
Optional reading	Visualization and ML (1)	Visual Exploration of Machine Learning Results using Data Cube Analysis ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models	N.A.
Optional reading	Visualization and ML (2)	Recent progress and trends in predictive visual analytics	N.A.
10/25	Feature Engineering (1)	Introduction to papers in this class Deep Feature Synthesis: Towards Automating Data Science Endeavors ExploreKit: Automatic Feature Generation and Selection	Xu Chu Jayant Prakash Andrea Hu
10/30	Feature Engineering (2)	Introduction to papers in this class One button machine for automating feature engineering in relational databases Feature Engineering for Predictive Modeling using Reinforcement Learning	Xu Chu Wendi Du Yue Zhang
Optional reading	Feature Engineering (3)	Discover Feature Engineering, How to Engineer Features and How to Get Good at It An Introduction to Variable and Feature Selection	N.A.
Part 3: Systems and ML
11/01	Managing ML Pipeline (1)	Introduction to papers in this class TFX: A TensorFlow-Based Production-Scale Machine Learning Platform ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows	Xu Chu Jennifer Blase Nidhi Menon
11/06	Managing ML Pipeline (2)	Introduction to papers in this class MODELDB: A System for Machine Learning Model Management Towards Unified Data and Lifecycle Management for Deep Learning	Xu Chu Prashanth Dintyala Pranshu Trivedi
Optional reading	Managing ML Pipeline (3)	A Berkeley View of Systems Challenges for AI	N.A.
11/08	Training Set Debug (1)	Introduction to papers in this class Training Set Debugging Using Trusted Items Flipper: A Systematic Approach to Debugging Training Sets	Xu Chu Yue Zhang Sneha Venkatachalam
11/13	Training Set Debug (2)	Introduction to papers in this class Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! Criticism for Interpretability	Xu Chu Nidhi Menon Yue Hu
11/15	Reducing Training Set	Introduction to papers in this class LightGBM: A Highly Efficient Gradient Boosting Decision Tree BlinkML: Approximate Machine Learning with Probabilistic Guarantees	Xu Chu Eric Qin Xiang Cheng
Optional Reading	Reducing Training Set	DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size	N.A.
11/20	Catastrophic forgetting	Introduction to papers in this class Overcoming catastrophic forgetting in neural networks Measuring Catastrophic Forgetting in Neural Networks	Xu Chu Eric Qin Zhuoran Yu
Optional reading	Debug ML	Why is machine learning 'hard'	N.A.
Course Project Presentations
11/22	N.A.	No Class (Thanksgiving)	N.A.
11/27	Final Project	TBD	Student
11/29	Final Project	TBD	Student
12/04	Final Project	TBD	Student

Disclaimer: The schedule could be subject to change as the semester progresses.