Download Why This Course?

CSE 4705
Artificial Intelligence
Jinbo Bi
Department of Computer Science & Engineering
http://www.engr.uconn.edu/~jinbo
1
The Instructor
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Ph.D. in Mathematics
Working experience
• Siemens Medical Solutions
• Department of Defense, Bioinformatics
• UConn, CSE
Contact: jinbo@ engr.uconn.edu, 486-1458 (office phone)
Research Interests:
• Machine learning, Computer vision, Bioinformatics
• Apply machine learning techniques in bio medical informatics
• Help doctors to find better therapy to cure disease
Color of flowers
Cancer,
Psychiatric
disorders,
…
http://labhealthinfo.uconn.edu/Ea
syBreathing
subtyping
GWAS
Today
Organizational details
Purpose of the course
Material coverage
Introduction of AI
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Course Syllabus
Go over syllabus carefully, and keep a
copy of it
Course website
http://www.engr.uconn.edu/~jinbo/Spring2015_Ar
tificial_Intelligence.htm
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Instructor and TAs
My office hours
Tue 1 – 3pm
Office Rm: ITE Building 233
Two TAs
Xingyu Cai ([email protected])
office hours Fri 2-3pm, contact him for the
place to meet
Xia Xiao ([email protected])
office hours Fri 2-3pm, ITEB 221
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Required Textbook
Attending the lectures is
highly encouraged, and
lectures highlight some
examples
Attending lectures is not a
substitute for reading the
text
Read the text in Chap 1 – 9,
because we follow them
tightly
6
Optional Textbooks
These textbooks cover some of the most
popular and fast-growing sub-areas of AI
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Prerequisite
Good knowledge of
programming
Data structures
Algorithm and complexity
Introductory probability and
statistics
Logic (discrete math)
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Slides
We do not always have slides
for later lecture
We use more lecture notes
than slides
Slides will be used to
demonstrate, and will be
available at HuskyCT after the
lecture
9
Marking Scheme
3 HW assignments:
30%
(programming based, and require time to complete)
1 Midterm:
1 Final Term project:
30%
40%
Curved
Curve is tuned to the final overall
distribution
No pre-set passing percentage
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Grading Arrangement
Xingyu Cai (BECAT A22)
Responsible for
HW 1
Mid-term exam
Final term projects
Xia Xiao (ITEB 221)
Responsible for
HW 2
HW 3
Please find the right TA for specific questions
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Questions?
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In-Class Participation
Finding errors in my lecture notes
Answering my questions and asking
questions
Come present your progress on term
projects
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Material Coverage
Two sets of topics:
classic versus state-of-the-art
Weeks 1 - 9:
Intelligent agents
Searching, informed searching
Constraint satisfaction problems
Logical agents
First-order logic
Read text chap 1-9 in the required textbook
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Material Coverage
Two sets of topics:
classic versus state-of-the-art
Weeks 10 - 14:
Basics in learning (supervised vs.
unsupervised learning)
Support vector machines
Artificial neural networks
These largely come from the optional
textbooks, will give slides to read
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Course Evaluation
Classic topics for weeks 1-9
3 HW assignments and 1 mid-term
60% of the final grade
Machine learning topics for weeks 10-14
A substantial term project
40% of the final grade
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Assignments
Each will have 4-10 problems from the
textbook (not all problems need coding)
Solutions will be published at HuskCT
when grades are returned
Each assignment will be given 1-2
weeks to complete, and grades will be
returned 1 week after turn in
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Term Projects
Substantial projects require teamwork.
Teams of 4-6 students should formed.
Each team needs to present at class
their project progress
Each team needs to submit a final
report together with necessary
codes/results for grading
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Term Projects
Three projects will be designed
All from real-world AI applications
Specifically big data applications
1)
2)
3)
Drug discovery (computational biology)
Disease understanding - Alzheimer’s
Disease from images
Robotics – learning to move Sarcos
robot arm
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Term Projects
Involve learning the background by
reading 1-2 papers
Involve programming with any of the
following languages/packages
Java
Python
Matlab
Or existing ML packages written in these
languages
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Questions?
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Why This Course?
A lot to list
Let us say
“This course will teach us foundational
knowledge of AI, so later we can do
research on top of it to
1. build intelligent agents (robots, search
engines etc.
2. understand human intelligence
3. handle massive BIG DATA
… … … “ Exemplar systems …..
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I want to design a machine that will be
proud of me – Danny Hillis
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DARPA Grand Challenge 2005
(driverless car competition)
Stanley won
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DARPA Urban Challenge 2007
(driverless car competition)
http://archive.darpa.mil/gr
andchallenge/
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Significant advances in NLP
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Search engines
Google search engine
Amazon (online purchase with product
recommendation)
Netflix (recommender systems)
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BIG DATA
Big data emerged from biology,
engineering, social science, almost
everywhere
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BIG DATA
Big data emerged from biology, engineering,
social science, almost every discipline
For instance, Biology: the big challenges of
big data, Nature 498, 255-260, 2013
Need powerful computers
to handle data traffic jams
Most importantly, need AI techniques to learn and
discover knowledge from data.
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What is AI
Views of AI fall into four categories
We focus on “acting rationally”
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Acting humanly
(Turing test)
Λ
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Acting humanly
(social robots)
MIT Leonardo Robot – isn’t this the cutest robot ever?
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Acting humanly
(social robots)
MIT Leonardo Robot – isn’t this the cutest robot ever?
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Thinking humanly
(cognitive modeling)
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Thinking rationally
(laws of thought)
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Acting rationally
(rational agents)
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Human has much stronger
perception than computers
Can you see a dalmation dog?
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Survey?
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