Schedule

This course schedule and reading list are tentative and might change as the quarter progresses.

The URLs of the online forms and the deadlines for submitting the paper reviews are listed alongside each paper; the deadline time is 8:59 AM PST on the deadline date.

If no URL is provided, you do not need to submit reviews for that particular paper, but it is still required reading and will be discussed during the lectures.

Papers without URLs that are also marked as “(Optional)” are not required readings, but they might come up in the lectures; at least skim reading these would be helpful for you.

Please try to attend all the lectures and participate in the discussions.

Lecture Date	Topic / Paper	Review Form URL	Review Deadline

01/08	Introduction and Overview
"	Data Management in Machine Learning: Challenges, Techniques, and Systems (Slide deck and Video)

	Scalable ML Analytics Frameworks
01/10	Towards a Unified Architecture for in-RDBMS Analytics	T1P1	01/10
"	The MADlib Analytics Library or MAD Skills, the SQL
01/15	Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing	T1P2	01/15
"	MLlib: Scalable Machine Learning on Spark
01/17	Scaling Distributed Machine Learning with the Parameter Server	T1P3	01/17
"	Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
"	Scalability! But at what COST? (Optional)

	Systems for Scalable Linear Algebra and Feature Engineering
01/22	SystemML: Declarative machine learning on MapReduce
"	SystemML: Declarative Machine Learning on Spark	T2P1	01/22
"	Towards Linear Algebra over Normalized Data (From questionnaire)
01/24	Materialization Optimizations for Feature Selection Workloads
"	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	T2P2	01/24

	Systems for Model Selection
01/29	Model Selection Management Systems: The Next Frontier of Advanced Analytics	T3P1
"	Automating Model Search for Large Scale Machine Learning		01/29

	Statistical Relational Learning Systems
01/31	Extracting Databases from Dark Data with DeepDive	T4P1	01/31
"	Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS

02/05	No class

	Deep Learning Systems
02/07	Densely Connected Convolutional Networks	T5P1	02/07
"	Understanding Neural Networks Through Deep Visualization (Video)
"	Visualizing and Understanding Recurrent Networks (Optional)
02/12	TensorFlow: A System for Large-Scale Machine Learning (Talk Slides)	T5P2	02/12
02/19	TVM: An Automated End-to-End Optimizing Compiler for Deep Learning	T5P3	02/19
"	Towards Unified Data and Lifecycle Management for Deep Learning

	Hardware-conscious ML Systems
02/19	From High-Level Deep Neural Models to FPGAs	T6P1	02/19
"	Communication-Efficient Learning of Deep Networks from Decentralized Data

	Reinforcement Learning Systems
02/21	Ray: A Distributed Framework for Emerging AI Applications	T7P1	02/21
"	Neural Architecture Search with Reinforcement Learning

	Model Serving Systems
02/26	Clipper: A Low-Latency Online Prediction Serving System	T8P1	02/26
"	MacroBase: Prioritizing Attention in Fast Data

	Data Sourcing for ML
02/28	Data Management Challenges in Production Machine Learning (Slide deck)	T9P1	02/28
"	The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets
03/05	Snorkel: Rapid Training Data Creation with Weak Supervision	T9P2	03/05
"	Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets

	ML Systems in Production
03/07	Machine Learning: The High-Interest Credit Card of Technical Debt	T10P1	03/07
"	Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
"	TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (Slide Deck)

03/12	Project Presentations
03/14	Project Presentations