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Special Dates

Make sure to become familiar with the Official Semester Dates. Some of the critical Semester Dates are:

• The Last Day to DROP Clases (without a ‘W’ grade) is October 11, 2022
• The Last Day to DROP Classes (with a ‘W’ grade) is November 14, 2022

Quiz Dates

• Quiz 1 Day (on lectures 1-7) is October 6, 2022
• Quiz 2 Day (on lectures 8-13) is October 27, 2022
• Quiz 3 Day (on lectures 15-23) is December 8, 2022

Attendance

Students are expected to attend each lecture in person according to the BU safety guidlines. All course material will be posted on Piazza. Ultimately, students are responsible for their own learning and, thus, for keeping up with the material.

Tentative lecture schedule

Date	Topic	Note
09/06	Lec 0: Course introduction
09/08	Lec 1: Database Concepts	Overview of basic concepts in Part I
09/13	Lec 2: Data Provenance	Read: Provenance: What's Next?
09/15	Lec 3: Introduction to Ray	Read: Ray: A Distributed Framework for Emerging AI Applications (Ray is the system we will be using in the assignments)
09/20	Lec 4: Discussion on Assignments #1 and #2	Read: Explaining Collaborative Filtering Recommendations
09/22	Hacking Day
09/27	Lec 5: Explaining Non-Answers	Read: Why not?
09/29	Lec 6: Data Causality	Read: Causality in Databases
10/04	Lec 7: Dataflow Provenance	Read: Explaining outputs in modern data analytics Optional: Provenance for generalized Map and Reduce workflows
10/04		DUE DATE: Assignment #1
10/06	Lec 8: Discussion on Assignment #1 Quiz #1 (during lecture)	Common mistakes in Assignment #1
10/11	No Lecture	Substitute Monday
10/13	Lec 9: Machine Learning Concepts	Overview of basic concepts in Part II
10/18	Lec 10: Generalized Additive Models	Read: Intelligible Models for Classification and Regression Watch: The Science Behind InterpretML: Explainable Boosting Machine
10/20	Lec 11: Explaining Classification Results (LIME)	Read: “Why should I trust you?” Explaining the predictions of any classifier Watch: The Science Behind InterpretML: LIME
10/21		DUE DATE: Assignment #2
10/25	Lec 12: Interpreting Model Predictions (SHAP)	Watch: The Science Behind InterpretML: SHAP Optional: A unified approach to interpreting model predictions
10/27	Lec 13: Discussion on Assignment #3 Quiz #2 (during lecture)
11/01	Lec 14: Guest Lecture by Bojan Karlas (Harvard Medical School)	Data Systems for Managing and Debugging Machine Learning Development Workflows
11/03	Lec 15: Distributed Systems Concepts	Overview of basic concepts in Part III
11/08	Lec 16: Causal Profiling	Read: Coz: finding code that counts with causal profiling Optional: SOSP'15 talk
11/10	Lec 17: Distributed System Tracing	Read: Dapper: A large-scale distributed systems tracing infrastructure Optional: X-Trace: A pervasive network tracing framework
11/11		DUE DATE: Assignment #3
11/15	Lec 18: Distributed System Tracing (cont.) Discussion on Assignment #4	Read: Pivot Tracing: Dynamic causal monitoring for distributed systems
11/17	Hacking Day	Office hours during lecture time
11/22	Lec 19: Blocked Time Analysis	Read: Making sense of performance in data analytics frameworks Optional: Scalability! But at what COST?
11/24	No Lecture	Thanksgiving Recess
11/29	Lec 20: Critical Path Analysis	Read: SnailTrail: Generalizing critical paths for online analysis of distributed dataflows
12/01	Lec 21: Guest Lecture	TBA
12/06	Lec 22: Log-based Performance Analysis	Read: The Mystery Machine: End-to-end performance analysis of large-scale internet services
12/08	Lec 23: Black-Box Performance Analysis Quiz #3 (during lecture)	Read: Performance debugging for distributed systems of black boxes
12/09		DUE DATE: Assignment #4