This deck will provide logistic of the class and very high level Apache SystemML information.

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
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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