1. Lecturer: Geoff Hulten TAs: Alon Milchgrub, Andrew Wei
CSEP 546 Machine Learning
Lecturer: Geoff Hulten
TAs: Alon Milchgrub, Andrew Wei

2. Logistics
Course website:
Canvas site:
Discussion board:

3. Introducing Myself Geoff Hulten ghulten@cs.washington.edu
What I’ve worked on…
Why I’m here…

4. Introducing our TAs Alon Milchgrub Quick Intro Office hours:
Quick Intro
Office hours:
Tues: 5:30 – 6:30 CSE 674
Andrew Wei
Quick Intro
Office Hours:
Tues: 4:30 – 6:20 CSE 007
Virtual hours for people viewing from MS? Mail me.

5. Introducing the Class What types of jobs? Machine learning experience?
Engineering?
Data science?
Management/PM?
Other?
Machine learning experience?
This is my first class?
Several classes / exploration?
Do it for a living?
Math?
Not really?
Can do?
I think in math?
Python?
Never used?
Some experience?
No problem?

6. Overview of the Course
Learn important machine learning algorithms (the tools)
2) Learn how to produce models (use the tools)
3) Learn how to produce working systems (ML Engineering)

7. Lecture Overview Subject to change Lecture Topic 1 Introduction
Overview of machine learning
Basics of Evaluating Models
Logistic Regression 2
Feature Engineering
Naïve Bayes
ROC Curves and Operating Points 3
Implementing with Machine Learning
Bounds and Comparing Models
Defining Success with ML systems 4
Decision Trees
Intelligent User Experiences
Design Pattern - Closed Loop 5
Overfitting and Underfitting
Design Pattern - Adversarial
Ensembles 1 - Bagging & Random Forests
Lecture
Topic 6
Ensembles 2 - Stacking & Boosting
Design Pattern - Corpus Based
Basics of Computer Vision
Clustering and Instance Based 7
Neural Networks
Approaching an ML Problem 8
Neural Network Architectures
Intelligence Architectures
Intelligence Management 9
Reinforcement Learning
Orchestrating Intelligent Systems 10
Design Pattern - Ranking
Other Important Machine Learning Algorithms
Review of the Course
Subject to change

8. Assignments Logistic Regression Feature Engineering (text)
Decision Trees
Ensembles (Random Forests)
Clustering & Instance Based
Feature Engineering (Vision)
Neural Networks
Reinforcement Learning
Model building & interpreting
And several Kaggle style competitions

9. Evaluation Assignments:
There will be reading assignments every week (~10% of grade) and coding / modeling assignments most weeks (~40% of final grade) and three Kaggle style competitions (~25% of final grade).
Assignments due two weeks after they are assigned. Except (possibly) for the last assignment, which is due before the start of the final lecture (so we can submit final course grades in a timely fashion).
Clarity of communication is critical in machine learning, so your answers must be concise and easy to follow. If the TA can’t evaluate the answers in reasonable time they will have to give reduced credit.
Exam:
There will be an exam worth ~25% of the final grade (although you must score at least 50% on the exam to pass the course).
This will be online and timed (2 – 3 hours). Tentatively scheduled for Dec 9th (complete it any time before midnight)
This exam will be based on the assignments, readings, and lectures.

10. The Textbooks and why…
All royalties to be donated…