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CSE 4705Artificial Intelligence
CSE 4705Artificial Intelligence
Jinbo BiDepartment of Computer Science &
Engineeringhttp://www.engr.uconn.edu/~jinbo
The InstructorThe Instructor• 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
subtyping GWAS
Color of flowers
Cancer, Psychiatric
disorders, …
http://labhealthinfo.uconn.edu/EasyBreathing
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TodayToday
Organizational details
Purpose of the course
Material coverage
Introduction of AI
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Course SyllabusCourse Syllabus
Go over syllabus carefully, and keep a copy of it
Course website http://www.engr.uconn.edu/~jinbo/
Spring2015_Artificial_Intelligence.htm
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Instructor and TAsInstructor and TAs
My office hoursTue 1 – 3pmOffice Rm: ITE Building 233
Two TAsXingyu Cai ([email protected])
office hours Fri 2-3pm, contact him for the place to meetXia Xiao ([email protected])
office hours Fri 2-3pm, ITEB 221
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Required TextbookRequired Textbook
Attending the lectures is highly encouraged, and lectures highlight some examplesAttending lectures is not a substitute for reading the textRead the text in Chap 1 – 9, because we follow them tightly
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Optional TextbooksOptional Textbooks
These textbooks cover some of the most popular and fast-growing sub-areas of AI
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PrerequisitePrerequisite
Good knowledge of programmingData structuresAlgorithm and complexityIntroductory probability and statisticsLogic (discrete math)
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SlidesSlides
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
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Marking SchemeMarking Scheme
3 HW assignments: 30%
(programming based, and require time to complete)
1 Midterm:30%1 Final Term project: 40%
CurvedCurve is tuned to the final overall distributionNo pre-set passing percentage
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Grading ArrangementGrading ArrangementXingyu Cai (BECAT A22)Responsible for
HW 1Mid-term examFinal term projects
Xia Xiao (ITEB 221)Responsible for
HW 2HW 3
Please find the right TA for specific questions
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Questions?Questions?
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In-Class ParticipationIn-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 CoverageMaterial CoverageTwo sets of topics:
classic versus state-of-the-art
Weeks 1 - 9: Intelligent agentsSearching, informed searchingConstraint satisfaction problemsLogical agentsFirst-order logic
Read text chap 1-9 in the required textbook
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Material CoverageMaterial CoverageTwo sets of topics:
classic versus state-of-the-art
Weeks 10 - 14: Basics in learning (supervised vs. unsupervised learning)Support vector machinesArtificial neural networks
These largely come from the optional textbooks, will give slides to read
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Course EvaluationCourse EvaluationClassic topics for weeks 1-9
3 HW assignments and 1 mid-term60% of the final grade
Machine learning topics for weeks 10-14
A substantial term project40% of the final grade
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AssignmentsAssignments
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 ProjectsTerm 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 ProjectsTerm Projects
Three projects will be designedAll from real-world AI applicationsSpecifically big data applications
1) Drug discovery (computational biology)
2) Disease understanding - Alzheimer’s Disease from images
3) Robotics – learning to move Sarcos robot arm
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Term ProjectsTerm Projects
Involve learning the background by reading 1-2 papersInvolve programming with any of the following languages/packages
JavaPythonMatlabOr existing ML packages written in these languages
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Questions?Questions?
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Why This Course?Why This Course?
A lot to listLet 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
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)
DARPA Grand Challenge 2005 (driverless car competition)
Stanley won
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DARPA Urban Challenge 2007 (driverless car competition)
DARPA Urban Challenge 2007 (driverless car competition)
http://archive.darpa.mil/grandchallenge/
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Significant advances in NLPSignificant advances in NLP
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Search enginesSearch engines
Google search engine
Amazon (online purchase with product recommendation)
Netflix (recommender systems)
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BIG DATABIG DATA
Big data emerged from biology, engineering, social science, almost everywhere
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BIG DATABIG DATABig data emerged from biology, engineering, social science, almost every disciplineFor 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 AIWhat is AI
Views of AI fall into four categories
We focus on “acting rationally”
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Acting humanly (Turing test)Acting humanly (Turing test)
Λ
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Acting humanly (social robots)Acting humanly (social robots)
MIT Leonardo Robot – isn’t this the cutest robot ever?
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Acting humanly (social robots)Acting humanly (social robots)
MIT Leonardo Robot – isn’t this the cutest robot ever?
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Thinking humanly (cognitive modeling)Thinking humanly (cognitive modeling)
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Thinking rationally (laws of thought)Thinking rationally (laws of thought)
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Acting rationally (rational agents)Acting rationally (rational agents)
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Human has much stronger perception than computersHuman has much stronger perception than computers
Can you see a dalmation dog?
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Survey?Survey?