This document provides details about an Apache SystemML class being offered in the summer of 2016. The class aims to teach scalable machine learning using Apache SystemML. It will cover SystemML usage, hands-on exercises for developing machine learning algorithms, and advanced aspects of the SystemML internals. The class is scheduled to take place over 8 sessions from June to August, covering topics like regression, classification, clustering, and the SystemML architecture, optimizer, and runtime.

1. Apache SystemML Class
”I predict what you will do next summer.”
Summer 2016
1

2. Class Description
• Goal
• Teach scalable machine learning with Apache SystemML
• Attract potential contributors
• Audience
• Initially summer interns, but goal of developing / folding into
University class
• Duration ~16 hours
• Content
• Development of scalable machine learning algorithms
• SystemML usage and hands-on exercises
• Advanced SystemMLinternals
• Office hours
• At Adlab: Thursday, 4-5 pm (may be expanded as demanded)
2

3. Outline
1. SystemML Primer
2. Machine Learning Algorithms
3. Advanced SystemML Internals
3

4. SystemML Primer
• Goal
• Teach enough DML, SystemML usage, and Spark for
people to be able to write and run SystemML algorithms
on Spark and understand its execution.
• Content
• DML syntax
• SystemML usage
• Some Spark
4

5. Machine Learning Algorithms
• Descriptive Statistics, Data Preparation, and
Train/Test/Cross-Validation
• Regression
• Classification
• Clustering &
Matrix Factorization
5
For each session / chosen algorithm have a
similar structure:
• Possible Applications
• Math / Alternatives / Discussion
• DML formulation
• Data generation
• Hands-on exercises
• Performance
• Accuracy

6. Advanced SystemML Internals
• Architecture
• Compiler
• Rewrites
• Optimizer
• Runtime
• Buffer pool
• Storage
• Advanced Operators
• Spark Backend
• Performance debugging
6

7. 7
S# /
Date
Category Title Content Instructor
S# /
Date
Category Title Content Instructor
S1
6/21:
9-12 am
R:
G1-404
SystemML
Primer
Scalable
Machine
Learning with
Apache
SystemML
• Intro ML
• DML
• SystemML usage
• Architecture
Berthold
Reinwald,
Nakul
Jindal
S5
7/18
4-6
R:
ML Algs
Clustering &
Matrix
Factorization
• kMeans, mf,
ALS, PCA, …)
• DML
• Data gen
• Hands-on
• Perf &
Accuracy
Alexandre
Evfimievski,
Prithvi Sen
S2
6/27:
4-6 pm
R:
ML Algs
Data Prep,
Descriptive
Statistics, and
Train/Test/Cr
oss-validation
• Math
• DML
• Data-gen
• Hands-on
• Perf & Accuracy
Faraz
Makari
Manshadi
S6
7/25
4-6 pm
R:
SystemML
Internals
Apache
SystemML
Architect.
• Architecture
• Hops/Lops
• CP/Cluster
Berthold
Reinwald,
Niketan
Pansare
S3
7/5:
4-6
R:
ML Algs Regression
• Linear, log., GLM,
Cox, Time series; CG
method
• DML
• Data-gen
• Hands-on
• Perf & Accuracy
Alexandre
Evfimievski
S7
8/1
4-6 pm
R:
SystemML
Internals
Apache
SystemML
Optimizer
• Rewrites
• Optimizer
• Cost model
Matthias
Boehm,
Arvind Surve
S4
7/11:
4-6
R:
ML Algs Classificat.
• NaïveBayes, SVM,
decTree, RF
• DML
• Data-gen
• Hands-on
• Perf & Accuracy
Prithvi Sen
S8
8/8
4-6 pm
R:
SystemML
Internals
Apache
SystemML
Runtime
• Buffer pool
• Storage
• Spark
backend
• Matrix block
lib
• Performance
debugging
Matthias
Boehm,
Arvind Surve