BAIL504 Data Visualization Lab
Course Learning Objectives
● Understand the Importance of data Visualization for business intelligence and decision making.
● Learn different approaches to understand the importance of visual perception.
● Learn different data visualization techniques and tools.
● Gain knowledge of effective data visuals to solve workplace problems.
SYLLABUS COPY
MODULE - 1
Intelligent Agents Agents and Environments, Good Behaviour: The Concept of Rationality, The Nature of Environments, The Structure of Agents Problem Solving : Game Paying
MODULE - 2
Uncertain knowledge and Reasoning: Quantifying Uncertainty, Acting under Uncertainty , Basic Probability Notation, Inference Using Full Joint Distributions, Independence , Bayes’ Rule and Its Use The WumpusWorld Revisited,
MODULE - 3
Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. Neural networks and genetic algorithms Brief history and Evolution of Neural network, Biological neuron, Basics of ANN, Activation function, MP model.Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. Neural networks and genetic algorithms Brief history and Evolution of Neural network, Biological neuron, Basics of ANN, Activation function, MP model.
MODULE - 4
Recommender System Datasets, Association rules, Collaborative filtering, User-based similarity, item-based similarity, using surprise library, Matrix factorization Text Analytics Overview, Sentiment Classification, Naïve Bayes model for sentiment classification, using TF-IDF vectorizer, Challenges of text analytics
MODULE - 5
Clustering Introduction, Types of clustering, Partitioning methods of clustering (k-means, k-medoids), hierarchical methods Instance Based Learning: Introduction, k-nearest neighbour learning(review), locally weighted regression, radial basis function, cased-based reasoning,
Course outcome
At the end of the course the student will be able to:
CO 1. Demonstrate the fundamentals of Intelligent Agents
CO 2. Illustrate the reasoning on Uncertain Knowledge
CO 3. Explore the explanation-based learning in solving AI problems
CO 4. Apply effectively ML algorithms to solve real world problems.
CO 5. Apply Instant based techniques and derive effectively learning rules to real world problems.
Suggested Learning Resources
Textbooks 1. Artificial Intelligence, A Modern Approach, Stuart J. Russell and Peter Norvig, Third Edition, Pearson, 2010 2. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 2013 3. Machine Learning, Anuradha Srinivasaraghavan, VincyJoeph, Wiley 2019 4. Machine Learning using Python ,Manaranjan Pradhan, U Dinesh Kumar, Wiley 2019 Reference 1. An Introduction to Multi Agent Systems, Michael Wooldridge, Second Edition, John Wiley & Sons