Facebook Twitter Linked In Instagram Youtube

Industry 4.0 is here. Everyone will need to become familiar with AI (Artificial Intelligence). IUCEE is conducting a Pilot “Introductory Course on Artificial Intelligence” for First Year college students in any discipline. This will be taught by Dr. Vemuri Rao, University of California Davis (Retd).

IUCEE Consortium institutions were invited to identify one faculty member and 20 first year students. The goal is for the faculty member to learn to teach such a course in the future, while mentoring the students as well as grading the assignments during the Pilot course. The course can be a value added course for extra credit or included in the curriculum, depending on the ability of the institution do so.

The Pilot is offered FREE for paid IUCEE Consortium Members.

Course Description

This is an introductory course on AI for entering freshmen from any academic discipline.
This course can be offered for 1 credit or 2 credits (semester or quarter). The one-credit course will be primarily a series of one-hour weekly webinars (say 10, in an academic Quarter) exploring the various facets of AI and its impact on modern life. The two-credit course will be the same series of webinars plus a team project.

AI is everywhere. All major companies are heavily invested in AI. But people are worried that robots will, one day, take over. AI is too important to be left alone in the hands of programmers and data scientists. Other vested interests from other disciplines should exert influence on AI and democratize it. The purpose of this course is to provide a broad background on what AI is all about, its implications, how to manage it to derive the best out of this emerging technology.

This course is aimed at freshers from any academic discipline. No programming is required. Familiarity with the Internet and basic computer skills are assumed. (Logging on a computer, web browsing, searching for information, checking e-mail, word processing, spreadsheet like Excel, basic math skills like graphing, statistical skills like finding averages, medians, etc. are assumed.)

The student’s responsibility is to attend all webinars and spend at least one hour a week/one- credit doing homework or project. Grading will be Pass/No Pass. A Pass will be awarded if the student attends 90% of the webinars and satisfactorily completes 90% of homework/project. The homework/project shall be graded by a local instructor. There are no mid-term or final examinations.

Homework/Projects

Each student does his/her own homework assignment
Three students form a team to do projects.
The students pretend that they are writing a research proposal for funding (by the govt. or by a venture capitalist.) In order to get funded, they have to articulate an idea, do some preliminary work (gather data, develop a preliminary model, show some preliminary results and argue why they should get the funding.) It is a mock exercise. Their mock research proposal should not exceed 4 pages.

Example Projects: (a) Build a robot that recommends a book that suits your taste, (b) Build a robot that looks at newspapers and extracts and compiles information that suits your interests, (c) build a tool that advises a farmer whether to plant seeds or not during the next week, (d) build a tool that tells you how long you have to wait for the next city bus. This list can be expanded. The point is this. None of these things can be done in 10 weeks. What the students have to do is to come up with a paper design of what is needed and how to go about it. There is no correct answer and there is no unique answer.

The topics for the webinars are selected to cover the general trends of AI. The webinars will give a cultural introduction to AI and also talk about some problem-solving techniques. The student projects need not be linked to the webinar topics. The list of topics for 10 webinars is given below.

1. Pictorial Intro to AI
2. AI Eco System
3. Intelligence from Data
4. Search Engines
5. Excel Tutorial
6. Clustering with Excel
7. Perceptron and Excel
8. Use Cases in Machine Learning
9. Building Intelligent Machines
10. Deep Learning

Instructor

Dr. Rao Vemuri
University of California, Davis
https://web.cs.ucdavis.edu/~vemuri/

Education

• University of California, Los Angeles, CA 1968, Ph.D., Engineering
• University of Detroit, Detroit, MI 1963, M.S., Engineering
• Andhra University, India 1958, B.E., Electrical Engineering

Professional Experience

2013-2014Vietnam Educational Foundation; US Faculty Scholar
2012-2014Third Eye Consulting, Palladion, Consultant
2011-presentIUCEE,Faculty Facilitator
2010-2011Fulbright Scholar Program; Fulbright-Nehru Lecturer
1986 – 2011Department of Applied Science, Professor
Dept. of Computer Science (Joint appt.)
University of California, Davis, CA, USA
1986 – 2007Lawrence Livermore National Labs, Computer Scientist
(Joint appointment with UC Davis)
2000-2002Smartifacts. LLC, Co-founder and CEO
(Concurrent with UC Davis)
1996 – 1999Dept of Applied Science Vice Chair and Graduate Adviser
1981 – 1986TRW, Inc., Redondo Beach, CA; Subproject Manager
Summer 1980Patrick Air Force Base, FL; AFOSR Fellow
1979Syracuse University, Syracuse; Visiting Professor
1973 – 1981Department of Computer Science; Associate Professor
School of Advanced Technology
SUNY Binghamton, NY
1970 – 1973Dept. of Aero & Astronautics and  Electrical Engineering Department;
Assistant Professor; Purdue University, W. Lafayette, IN
1963 – 1964RCA, Indianapolis, IN, USA; Senior Engineer
1958 – 1961Bhilai Steel Works, Bhilai, India; Junior Engineer

Program Calendar

There are no upcoming events at this time.

Program Co-ordinator

Dr. Krishna Vedula
Apply for Artificial Intelligence for All