Artificial Intelligence

Start Date
25/01/2019

End Date
10/05/2019

Enrollment End Date
24/02/2019

No. of
Enrollments
2808 students

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Course Syllabus
(PDF Format)

Created by

Bhushan Trivedi
Gujarat University
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Course Language
English
Course Type
Scheduled
Video transcripts
English
Course Category
Engineering, Science
Learning Path
Undergraduate
Course Length
60 Hours
Weekly time commitments
4 Hours
Course Completion
Yes, after passing all tests.
Exam Date
To be announced
Credits
4

204

Tutorials

49

Tests

0

Assignment

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Article

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Weekly Reading list

Overview

Hi friends here is a course on Artificial Intelligence. Computer science and engineering is a discipline where many subjects change over years but AI is one such course which is still part of the curriculum. In fact, in recent years there is a surge in interest in AI. There are many reasons for continued interest in AI and recent resurgence. AI is an interesting domain which provides solutions which are not possible using conventional methods. ? If you have taken some programming courses before, you probably have developed a few solutions yourself for some problems. There are some problems which you probably have not tried and or even if tried, you could have seen that there is no apparent algorithm for solving that problem. There are many problems which we can easily solve but computer programs struggle. For example, we meet a friend after a very long period and he has grown in all dimensions but we are able to still recognize him. Can an image processing software do so? When we have a humungous amount of network logs related to security information, can we write a program to check for intrusion in the network? If a child can look at a few examples of something, a bag for example, and learn what a bag is, can a computer program do the same? Conventional methods of coding cannot solve these problems; we need to find different ways to solve such problems. Interestingly humans can solve these problems without much of an effort. That means if we want to solve these types of problems we need to see and mimic humans problem-solving process. When we say mimicking humans we do not say that we will do exactly as humans do but take inspiration from it. An airplane is inspired by birds flying but it does not flap wings nor does directly move up and come down. In this module, we will see some interesting ways to solve these problems. In recent years many new research areas emerged which demanded solutions of this type. That has brought a resurgence of interest in AI in researchers as well. So either you would like to solve unconventional or human-like problems or produce a research solution using such methods, it is imperative that you need to learn AI. In recent years there is an interesting domain emerged known as big data which is dealing with voluminous, fast moving and a variety of data. An associated field is known as Analytics which looks at a large amount of data and find patterns from that data to make decisions. For example collection of patient’s data churned and patterns are found to see which situation leads to which types of diseases. In fact, some recent research found interesting relations between gnomes and probability of diseases and thus can predict if a person will have a typical disease in the future. Actress Angelina Jolly’s case is quite well known. All that is due to research in AI and related areas. 

 

Let us see what this course offers you. Once you complete the course, you will be able to describe a complex problem in the form of a state space, write rules which allow you to move around in state space and reach to the solution from the initial position. As there are no direct methods to solve most AI problems, it rests on searching through state space to reach to the final state. Unfortunately, search unless used for trivial problems runs into a problem. A number of states that it has to manage goes out of proportion so it becomes impossible to search in real time. One needs to augment the search technique with heuristics to find shortcuts during the search process. There are many types of problems and many types of solutions. We will look at quite a few of them and also see which type of problem need which type of solutions. We will look at neural networks which mimic functions of the human brain. Research in neural networks has helped us solve problems like signature recognition and face recognition. Not only search, but it is also important to find some way to store and process knowledge for making decisions. There are many knowledge representation methods which we will be learning in the due course. There will be an introduction to planning and game playing which are quite common problems which are solved using AI techniques. We will also look at Genetic Algorithms and few other popular techniques for solving specific problems. We will look at methods which can help us solve problems which are inexact in nature, requires fuzzy reasoning and uncertainties. We will finish off with an introduction to machine learning which is sprouted out of AI and now almost have become a discipline on its own. 

Last thing. How do you know this course is for you? If you are an MCA or a Computer Engineering or Information Technology student of engineering, AI is one of the courses offered in your discipline. Not only that, if ever you encounter a problem that cannot be solved using conventional techniques in your job, you may try using one of the methods that we will explore in due course.

We will have almost 15 weeks to complete this course. For each week, we will have 2-3 modules containing video lectures, e-text, and recapping exercises. You will also have some pointers for further reading as and when appropriate. At the end of each week, you will have a test to see how much you learn that week. At the end of the 15thweek, you will have a final test that you will have to clear to get the degree. There are total 5 credits for this course.

What do you need to know before taking this course? A bit of programming knowledge is required. If you have studied one programming language, it is enough. Some idea about data structures and basic maths is also required. You may refer to the introduction text to see what we are going to cover each week. Each week will introduce two to three modules of about 30 minutes each. 

To access the content, please enroll in the course.

Faculty

Bhushan Trivedi


Prof. Bhushan Trivedi, Ph. D. is acting as dean, school of computer technology at GLS University. He obtained his MCA degree from M S University, Vadodara, in 1984.   He has received his Ph. D. degree from Hemchandracharya North Gujarat University in 2008. Three of his books are published by Oxford University Press. First is in ANSI C++, secondly is on Computer Networks and the third is on Data Communication and Networks. Prof. Trivedi is supervising 8 Ph.D. Students currently, seven students have obtained their Ph. D. degrees while three students have submitted their thesis. He has filed four patents out of them 3 are published already. Prof. Trivedi has published about 87 research papers.His research interest includes pedagogy, security, intrusion detection and prevention, expert systems and neural networks. He has conducted about 23 workshops across India on "Effective Teaching". He has conducted about 10 workshops on research and related areas, about 8 workshops on "How to debug your network using Wireshark and TCPDUMP". Prof. Trivedi has given numerous speeches on Information Security, the need for security policies and various other subjects related to security, research, and pedagogy.Prof Trivedi is an active life member of Computer Society of India, was chairman Ahmedabad Chapter in 2007. Prof. Trivedi received an award for the work on effective teaching by IUCEE in 2009. He is also given Chapter Petron award by Computer Society of India in 2011.  

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