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CSE 4705

Artificial Intelligence

Jinbo Bi

Department of Computer Science & Engineering

http://www.engr.uconn.edu/~jinbo

The 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/Ea

syBreathing

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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 (xingyu.cai@uconn.edu)

office hours Fri 2-3pm, contact him for the place to meet

Xia Xiao (xia.xiao@uconn.edu)

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

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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

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Marking Scheme

3 HW assignments: 30% (programming based, and require time to complete)

1 Midterm: 30%

1 Final Term project: 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) 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 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?