1. CSE 4705 Artificial Intelligence
Jinbo Bi
Department of Computer Science & Engineering

2. Psychiatric disorders, …
The Instructor
Ph.D. in Mathematics
Working experience
Siemens Medical Solutions
Department of Defense, Bioinformatics
UConn, CSE
Contact: engr.uconn.edu, (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, …

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

4. Course Syllabus Go over syllabus carefully, and keep a copy of it
Course website

5. Instructor and TAs My office hours Two TAs Tue 1 – 3pm
Office Rm: ITE Building 233
Two TAs
Xingyu Cai
office hours Fri 2-3pm, contact him for the place to meet
Xia Xiao
office hours Fri 2-3pm, ITEB 221

6. 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

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

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

9. 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

10. Marking Scheme 3 HW assignments: 30% 1 Midterm: 30%
(programming based, and require time to complete)
1 Midterm: %
1 Final Term project: 40%
Curved
Curve is tuned to the final overall distribution
No pre-set passing percentage

11. 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

12. Questions?

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

14. 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

15. Material Coverage Two sets of topics: Weeks 10 - 14:
classic versus state-of-the-art
Weeks :
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

16. 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

17. 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

18. 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

19. 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

20. 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

21. Questions?

22. 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 …..

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

24. DARPA Grand Challenge 2005 (driverless car competition)
Stanley won

25. DARPA Urban Challenge 2007 (driverless car competition)

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

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

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

30. BIG DATA
Big data emerged from biology, engineering, social science, almost every discipline
For instance, Biology: the big challenges of big data, Nature 498, , 2013
Need powerful computers to handle data traffic jams
Most importantly, need AI techniques to learn and discover knowledge from data.

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

32. Acting humanly (Turing test)Λ

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

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

35. Thinking humanly (cognitive modeling)

36. Thinking rationally (laws of thought)

37. Acting rationally (rational agents)

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

39. Survey?