| Modules |
Lectures |
Topics |
Literature |
Labs |
Assignments |
| Foundation |
Lecture 1 |
Introduction to Big Data Systems |
Applying for AWS Academy account |
Lab 1 |
Test 0 |
| Lecture 2 |
Big Data Learning Systems and Applications |
Term project ideas |
Lab 2 |
Torch Introduction, K-Means Walkthrough |
| Lecture 3 |
Distributed Systems*, Architectures*, Deep Learning Frameworks |
Chapter 1 (Tanenbaum), Chapter 2 (T.) |
Lab 3 |
Distributed |
| Lecture 4 |
Communication*, Parralelization* |
Chapter 4 (T.) |
Lab 4 |
Proposal / Due Data Exploration with Pandas & Matplotlib |
| Advanced Topics |
Lecture 5 |
Synchronization*, Consistency and Replications*, Fault Tolerance |
Chapter 6 (T.), Chapter 7 (T.), Chapter 8 (T.) |
Lab 5 |
LSTM Anomaly Detection |
| Lecture 6 |
Real Time Machine Learning |
Streaming Data Analysis |
Lab 6 |
Apache Storm |
| Lecture 7 |
Midterm Presentation |
|
|
Midterm Report Due |
| Lecture 8 |
High Performance Computing Architectures, Measuring Performance |
CPU, GPU, AI Chips |
Lab 7 |
Edge TPU |
| Term Project |
Lecture 9 |
Memory Technology, Vectorization |
Profiling Tool with Intel VTune |
Lab 8 |
Vectorization |
| Lecture 10 |
Message Passing Interface (MPI) |
Solving Bigger Problems by Scaling |
Lab 9 |
MPI |
| Lecture 11 |
Benchmarking, User Benchmarks, Industry Benchmarks |
Time Series |
Lab 10 |
MLPerf |
| Lecture 12 |
Discussion of Parallel Thinking, Data Processing Pipeline (preparation, processing, management analysis, and visualization) |
HPC and Big Data |
Lab 11 |
End-to-end Analytics |
| Lecture 13 |
Big Data Frameworks |
HPC and Big Data |
Lab 12 |
Apache Hadoop and Spark |
| Lecture 14 |
Discussions on the Convergence of HPC, Big Data, and Machine Learning |
Digital Twin |
|
|
| Lecture 15 |
Final Presentation |
Project Review |
|
Final Report Due |