Introduction:
What is AI? Foundations and History of AI
Problem‐solving:
Problem‐solving agents, Example problems, Searching for Solutions, Uninformed Search Strategies: Breadth First search, Depth First Search,
Textbook 1: Chapter 1- 1.1, 1.2, 1.3
Textbook 1: Chapter 3- 3.1, 3.2, 3.3, 3.4.1, 3.4.3
Informed Search Strategies:
Greedy best-first search, A*search, Heuristic functions. Introduction to Machine Learning , Understanding Data
Textbook 1: Chapter 3 - 3.5, 3.5.1, 3.5.2, 3.6
Textbook 2: Chapter 1 and 2
Basics of Learning theory
Similarity Based Learning
Regression Analysis
Textbook 2: Chapter 3 - 3.1 to 3.4, Chapter 4, chapter 5.1 to 5.4
Decision Tree learning
Bayesian Learning
Textbook 2: Chapter 6 and 8
Artificial neural Network
Clustering Algorithms
Textbook 2: Chapter 10 and 13
Course Outcomes Course Skill Set)
At the end of the course the student will be able to:
CO 1. Apply the knowledge of searching and reasoning techniques for different applications.
CO 2. Have a good understanding of machine leaning in relation to other fields and fundamental issues and challenges of machine learning.
CO 3. Apply the knowledge of classification algorithms on various dataset and compare results
CO 4. Model the neuron and Neural Network, and to analyze ANN learning and its applications.
CO 5. Identifying the suitable clustering algorithm for different pattern
Assessment Details (both CIE and SEE)
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks (duration 01 hours) OR Suitable Programming experiments based on the syllabus contents can be given to the students to submit the same as laboratory work( for example; Implementation of concept learning, implementation of decision tree learning algorithm for suitable data set, etc…)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks and will be scaled down to 50 marks (to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall be proportionally reduced to 50 marks.
2. There will be 2 questions from each module. Each of the two questions under a module (with a maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Stuart J. Russell and Peter Norvig, Artificial Intelligence, 3rd Edition, Pearson,2015
2. S. Sridhar, M Vijayalakshmi “Machine Learning”. Oxford ,2021
Reference:
1. Elaine Rich, Kevin Knight, Artificial Intelligence, 3rdedition, Tata McGraw Hill,2013
2. George F Lugar, Artificial Intelligence Structure and strategies for complex, Pearson Education, 5th Edition, 2011
3. Tom Michel, Machine Learning, McGrawHill Publication.