Tentative Schedule (subject to changes)
| Date | Topic | Content | Presentor |
|---|---|---|---|
| W1: Aug 21 | Lecture | Course Introduction and Logistics (slides) | Instructor |
| W1: Aug 23 | Lecture | Research Skills (Part 1) (slides) (handout) | Instructor |
| W2: Aug 28 | Lecture | Approximate Query Processing (slides) | Instructor |
| W2: Aug 30 | Approximation | BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data (slides) | Siddhant, Tanvi, Xiaoyue, Tony |
| W3: Sep 04 | No Class (Labor Day) | ||
| W3: Sep 06 | Approximation | AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics (slides) | Preethi, Chitti, Aravind, Pearl |
| W4: Sep 11 | Approximation Project team due | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics (slides) | Maansi, Meghan, David, Chitti |
| W4: Sep 13 | Approximation | Hillview: A trillion-cell spreadsheet for big data (slides) | Hakesh, Aniruddha, Aravind |
| W5: Sep 18 | Lecture | Data Preprocessing and Labeling (slides) | Instructor |
| W5: Sep 20 | Data Producer | BoostClean: Automated Error Detection and Repair for Machine Learning (slides) | Siddharth, Siddhant, Brian |
| W6: Sep 25 | Project Proposal Proposal due | (slides) | |
| W6: Sep 27 | Data Producer | Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples (slides) | Aniruddha |
| W7: Oct 02 | Data Producer | Snorkel: Rapid Training Data Creation with Weak Supervision (slides) | David, Brian, Mahek, Vima, Amey, Siddharth |
| W7: Oct 04 | Lecture | Research Skills (Part 2) (slides) | Instructor |
| W8: Oct 09 | No Class (Fall Break) | ||
| W8: Oct 11 | Data Producer | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes (slides) | Daniel B., Vima, Yihan, Daniel L., Aniruddha, Preethi |
| W9: Oct 16 | Data Producer | Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence at Scale (slides) | Tanvi, Rachit, Faith, Siddharth, Yuxin, Daniel B., Preethi |
| W9: Oct 18 | Lecture | Approximate Nearest Neighbor Search (slides) | Instructor |
| W10: Oct 23 | ANNS | SLIDE: In Defense of Smart Algorithms over Hardware Acceleration for Large-scale Deep Learning Systems (slides) | Mahek, Vima, Meghan, Maansi, Daniel L. |
| W10: Oct 25 | ANNS | LSH Ensemble: Internet-Scale Domain Search (slides) | Hakesh, Siddharth, Faith, Daniel B. |
| W11: Oct 30 | ANNS | Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins (slides) | Yuxin, Aravind, Maansi, Daniel B., Mahek |
| W11: Nov 01 | Project Update | (slides) |
| W12: Nov 06 | Lecture | User Experience (slides) | Instructor |
| W12: Nov 08 | Data Consumer | SeeDB: efficient data-driven visualization recommendations to support visual analytics (slides) (hacker notebook) | Xiaoyue, Amey, Meghan, David, Yihan, Rachit, Brian |
| W13: Nov 13 | Data Consumer | Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows (slides) (hacker notebook) | Daniel, Pearl, Mahek, Brian, Rachit, Yuxin |
| W13: Nov 15 | Data Consumer | M4: A Visualization-Oriented Time Series Data Aggregation (slides) M4 Demo M4 client | Faith, Yihan, Tony, Pearl, Yuxin, Daniel |
| W14: Nov 20 | Data Consumer | MacroBase: Prioritizing Attention in Fast Data (slides) | Tony, Amey, Xiaoyue, Siddhant |
| W14: Nov 22 | No Class (Thanksgiving) |