Fall 2016 Computer Science 458: 11/14/2016
Per piazza posting, Jieung Kim office hours: Monday and
Wednesdays, 5:30 pm to 7:30pm. AKW 311.
Wednesday November 16th: David Swensen, Yale Investment Office.
Reading: Yale Endowment 2015
Pioneering Portfolio Management, Chapter 3: Investment
and Spending Goals
Chris Gunther (plus one)
Today's Guest Speaker:Alborz Geramifard,
Research Scientist, Amazon Alexa
Alborz Geramifard is currently a senior research scientist and
manager at Amazon leading the Boston branch of Alexa's conversational
AI. Before joining Amazon, he was a postdoctoral associate at MIT's
Laboratory for Information and Decision Systems. Alborz received his
PhD from MIT working with Jonathan How and Nicholas Roy on
representation learning and safe exploration in large scale sensitive
sequential decision-making problems in 2012. He finished his MSc at
University of Alberta under the supervision of Richard Sutton and
Michael Bowling in 2008, where he worked on data efficient online
reinforcement learning techniques. His research interests lie at
machine learning with the focus on reinforcement learning, natural
language understanding, planning, and brain and cognitive
sciences. Alborz was the recipient of the NSERC postgraduate
scholarships 2010-2012 program.
Echo is high performance fully voice controlled wireless speaker
created by Amazon that is designed for the home. It is very
convenient, always plugged in and ready to use. The technology
behind Echo lives on device as well as in the cloud and represents
some of the best in Natural Language Processing (NLP) technologies
today. Echo is extensible and supports a broad range of
functionalities out-of-the-box, such as music, wikipedia, to-do and
shopping lists, sports and weather information, generic question
answering and more. It features high performance keyword spotting,
automatic speech recognition, natural language understanding,
question answering and text-to-speech. This talk provides an
overview of speech and natural language processing activities at
Amazon around Echo and describe some of the core technologies and
research challenges our teams are facing.
Alborz's cheat sheet for acing the Amazon machine learning interview.
Final Project: Additional Specifications
Final projects are due the last day of reading period: Thursday
Be prepared to give a brief presentation of your project to
the class after Thanksgiving break. It should be 5-10 minutes,
and describe what you intend to do. If you have already implemented
something, all the better.
Post project proposal to class*v2 under assignments Once
you get approval, you may commence work.
Submit your proposal this week. Leave time for an iterative
- Your project should run in the zoo. If you need additional libraries or
software installed, you must clear it with the TF.
- You need to include a write-up explaining your project, including
how to run it, what it does, what is interesting or important about it,
how it explains the decision. Screen shots might be helpful as well.
- You can create a web site. In this case, it may not need to run
in the zoo. However, you need to submit all the code so that
it could theoretically be run in the zoo.
- Can you leverage a project for another course? It depends. You need to
check with me first.
- Can you change your project after your proposal? Well, yes. The
real question is what happens if you do that? The answer is again, it depends.
If your initial idea hits a wall and does not work, you probably should
figure out another way to go.
Final Project: Criteria
In no particular order:
- Originality. I appreciate the degree to which you come up
with new ideas. There are lots of ways to tackling the projects.
- Functionality. The program should work. It is best if we
can run it ourselves, such as on the zoo. At the very least, you
should provide ample evidence that the program does what it is
supposed to do.
- Explanation. The program should explain its behavior. A
good way to think about this is how you would explain your program's
output to someone observing your program in action.
- Relevance to course. The program should have a connection
to a topic or topics discussed in class, including the guest speakers.
The more direct the connection, the better. Note that the best
connection is through a method or technique, not a domain. Thus,
applying Monte Carlo simulation to medicine is better than applying
astrology to finance.
- Clarity. This is valued in thought, word, and deed. How
clear is your concept? Do you describe it well in your write-up?
Does your program clearly reflect your idea?
- Success. Actually, not so much. In a freshman chemistry
course, I selected a project of making polywater - a polymer form of
water which had been reported by Soviet scientists. The professor
encouraged me to pursue this and it failed. I was unable to replicate
the Soviet experiment. (As it turns out, neither was anyone else.)
My point is that you may learn from failure as well as from success.
Self-driving Cars: an introduction
Cognitive Models of Decision Making: VOTE
A Realistic Model of Rationality (More readable PDF version of above.)
Running VOTE on the zoo
An Intentional Arithmetic for Qualitative Decision
human.java (Java human class with