CSE 4705 Artificial Intelligence Jinbo Bi Department of Computer Science & Engineering http://www.engr.uconn.edu/~jinbo

Psychiatric disorders, … 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/EasyBreathing

Today Organizational details Purpose of the course Material coverage Introduction of AI

Course Syllabus Go over syllabus carefully, and keep a copy of it Course website http://www.engr.uconn.edu/~jinbo/Spring2015_Artificial_Intelligence.htm

Instructor and TAs My office hours Two TAs 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

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

Optional Textbooks These textbooks cover some of the most popular and fast-growing sub-areas of AI

Prerequisite Good knowledge of programming Data structures Algorithm and complexity Introductory probability and statistics Logic (discrete math)

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

Marking Scheme 3 HW assignments: 30% 1 Midterm: 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

Grading Arrangement Xingyu Cai (BECAT A22) Xia Xiao (ITEB 221) Responsible for HW 1 Mid-term exam Final term projects Xia Xiao (ITEB 221) HW 2 HW 3 Please find the right TA for specific questions

Questions?

In-Class Participation Finding errors in my lecture notes Answering my questions and asking questions Come present your progress on term projects

Material Coverage Two sets of topics: Weeks 1 - 9: 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

Material Coverage Two sets of topics: Weeks 10 - 14: 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

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

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

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

Term Projects Three projects will be designed All from real-world AI applications Specifically big data applications Drug discovery (computational biology) Disease understanding - Alzheimer’s Disease from images Robotics – learning to move Sarcos robot arm

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

Questions?

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 …..

I want to design a machine that will be proud of me – Danny Hillis

DARPA Grand Challenge 2005 (driverless car competition) Stanley won

DARPA Urban Challenge 2007 (driverless car competition) http://archive.darpa.mil/grandchallenge/

Significant advances in NLP Siri use your voice to send messages, set reminders

Search engines Google search engine Amazon (online purchase with product recommendation) Netflix (recommender systems)

BIG DATA Big data emerged from biology, engineering, social science, almost everywhere

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.

What is AI Views of AI fall into four categories We focus on “acting rationally”

Acting humanly (Turing test) Λ

Acting humanly (social robots) MIT Leonardo Robot – isn’t this the cutest robot ever?

Acting humanly (social robots) MIT Leonardo Robot – isn’t this the cutest robot ever?

Thinking humanly (cognitive modeling)

Thinking rationally (laws of thought)

Acting rationally (rational agents)

Human has much stronger perception than computers Can you see a dalmation dog?

Survey?