IT DEPARTMENT COURSES

This is IA KT 2020 BE IT VII

This is precursor to User Interaction Design (UID) design department elective course. It will serve as guideline for students to understand what they should expect to learn during this course.

Currently the course open to all BEIT 19-20 batch students to enrol.

Hope this will help students to get fair idea about the subject. 

This course is for Semester IV Information Technology offered during A Y 2019-20  Start Date: 07/01/2020.

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Inrastructure Security BE/IT (VII Sem  Odd ) Academic Year 2020-21

First Offerring : AY 2018-19 (AI)

Start Date: 16/07/2018

Faculty: Prof A S Kunte and Prof. P B Lad

Second Offerring: AY 2019-20 IS LAB

Start Date: 15/07/2019

Faculty: Prof A S Kunte

Third Offering: AY 2020-21 AI and IS LAB

Faculty: Prof A S Kunte and Prof. R S  Pachade

Course name: AI

Course Objectives: Students will try:

  1. To create appreciation and understanding of both the achievements of AI and the theory underlying those achievements
  2. To introduce the concepts of a Rational Intelligent Agent and the different types of Agents that can be designed to solve problems
  3. To review the different stages of development of the AI field from human like behavior to Rational Agents.
  4. To impart basic proficiency in representing difficult real life problems in a state space representation so as to solve them using AI techniques like searching and game playing.
  5. To create an understanding of the basic issues of knowledge representation and Logic and 
    blind and heuristic search, as well as an understanding of other topics such as minimal, resolution, etc. that play an important role in AI programs.
  6. To introduce advanced topics of AI such as planning, Bayes networks, natural language processing and Cognitive Computing.

Course Outcomes:  Students will be able to:

  1.  Demonstrate knowledge of the building blocks of AI as presented in terms of intelligent agents.
  2.  Analyze and formalize the problem as a state space, graph, design heuristics and select among different search or game based techniques to solve them.
  3.  Develop intelligent algorithms for constraint satisfaction problems and also design intelligent systems for Game Playing
  4.  Attain the capability to represent various real life problem domains using logic based techniques and use this to perform inference or planning.
  5. Formulate and solve problems with uncertain information using Bayesian approaches.
  6. Apply concept Natural Language processing to problems leading to understanding of cognitive computing.
Course name: IS LAB

Course Objectives:  Students will try:

  1. To introduce the concepts of a Rational Intelligent Agent and the different types of Agents that can be designed to solve problems.
  2. To impart basic proficiency in representing difficult real life problems in a state space representation so as to solve them using AI techniques.
  3. To make students understand various AI methods like searching and game playing and how to apply them to solve real applications.
  4. To explain to students the basic issues of knowledge representation and Logic so as to build inference engines.
  5. To impart a basic understanding of some of the more advanced topics of AI such as planning.
  6. To understand Bayes networks, natural language processing and introduce concept of cognitive computing.

Course Outcomes:  Students will be able to:

  1.  Design the building blocks of an Intelligent Agent using PEAS representation .
  2.  Analyze and formalize the problem as a state space, graph, design heuristics and select among different search or game based techniques to solve them.
  3. Develop intelligent algorithms for constraint satisfaction problems and also design intelligent systems for Game Playing
  4.  Attain the capability to represent various real life problem domains using logic based techniques and use this to perform inference or planning.
  5. Formulate and solve problems with uncertain information using Bayesian approaches.
  6. Apply concept Natural Language processing and cognitive computing for creation of domain specific ChatBots.

ITC 305 Paradigms and Computer Programming Fundamentals

Course Objectives:

The image shows course objectives for ITC305 course.

Course Outcomes:

Image shows course outcomes for ITC 305 course.


In this Course students will learn the following Modules

Module 1: Laplace Transform

Module 2: Inverse Laplace Transform

Module 3: Fourier Series

Module 4: Complex Variable

Module 5: Statistical Techniques

Module 6: Probability


This is a compulsory course for all SE IT 20 21 students as a prerequisite.