Artificial Intelligence in Business Applications

Syllabus

404 BA

ARTIFICIAL INTELLIGENCE IN BUSINESS APPLICATIONS

Unit 1

Introduction to AI and Programming Tools: Analytics Landscape, Complexity of Analytics, What Is Artificial Intelligence? Embedding AI into Business Processes, Basic Concepts of Artificial Intelligence Brain Science and Problem Solving, The History of AI, Benefits of AI Data Pyramid Property of Autonomy, The AI Revolution, Business Innovation with Big Data and Artificial Intelligence. AI and Predictive Analytics, Overlapping of Artificial Intelligence with Other Fields Ethics and Privacy Issues, Application Areas, AI and Society. Knowledge-Based Systems Knowledge Based Reasoning: Agents, Facets of Knowledge.                                                                               (7)

Unit 2

Logic and Inferences: Formal Logic, Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive Retrieval, Backward Chaining, Second Order Logic. Knowledge Representation: Conceptual Dependency, Frames, Semantic Nets. Reasoning Systems for Categories, Reasoning with Default Information. Propositional Logic & Predicate Logic - Syntax., Semantics , Computability and Complexity Applications and Limitations, Logic for Problem Solving, Logic Programming with PROLOG , PROLOG Systems and Implementations, Execution Control and Procedural Elements, Constraint Logic Programming, Simple Examples             (8)

Unit 3

Problem Solving, Search and Game Techniques: Problem Solving with AI, Study and Analysis of Various Searching Algorithms, Local Search in Continuous Spaces, Searching with Non-Deterministic Actions General Problem Solver, Gelernter's Geometry Theorem, STRIPS, ABSTRIPS, Search - Overview, Problem Representation State-Space Representation, Problem-Reduction Representation, Uninformed Search - Blind State-Space Search, Breadth-First Search, Uniform-Cost Search, Depth-First Search, Iterative Deepening, Heuristic Search, Greedy Search , A★-Search ,IDA★ Search. Games with Opponents- Minimax Search, Alpha-Beta-Pruning Non-Deterministic Games. Heuristic Evaluation Functions Game Trees, Optimal Search for an Optimal Solution. Conditions for Optimality: Admissibility and Consistency, Optimality of A*, Optimization Problems: Hill-Climbing Search Simulated Annealing, Local Beam Search, Recursive Best First Search, Pruning the CLOSED and OPEN Lists                                                                                                                          (10)

Unit 4

Machine Learning and Data Mining: Introduction - What is Machine Learning?, Supervised vs. Unsupervised Learning , Reinforcement Learning. Machine Learning Workflow, Learning Algorithms, Linear Regression K-Nearest Neighbor , Decision Trees, Feature Construction and Data Reduction, Random Forest, K-Means Algorithm, Gradient Boosting, Analyzing Big Data Different Deep Learning Models, Auto Encoders, Data Analysis, The Perceptron, a Linear Classifier, The Learning Rule, Optimization and Outlook , The Nearest Neighbor Method, Two Classes, Many Classes, Approximation, Case-Based Reasoning, Decision Tree Learning, Entropy as a Metric for Information Content, Learning of Appendicitis Diagnosis, Cross-Validation and Over Fitting, Learning of Bayesian Networks, Learning the Network Structure, The Naive Bayes Classifier, Clustering, Hierarchical Clustering, Data Mining in Practice                      (10)

Unit 5

Natural Language Processing & Neural Networks: Introduction to Natural Language Processing, Stages in NLP, NLP Models, Morphological Processing - Syntax and Semantics, Text Analytics, Sentiment Analysis, Syntactic Analysis (Parsing), Semantic Interpretation, Discourse and Pragmatic Processing, Text Classification, Implementation Aspects of Syntactic Analysis (Parsing), Application of NLP in Machine Translation, Information Retrieval and Big Data Information Retrieval. Learning: Supervised, Unsupervised and Reinforcement Learning. Use Cases of NLP, Applications of NLP in Business Customer Service, Reputation Monitoring. Market Intelligence, Sentiment Technology in Business. Artificial Neural Networks - Concept, Feed forward and Feedback ANNs, Error Back Propagation, Boltzmann Machine, Deep Neural Network and Tools, Hopfield Networks, Application to a Pattern Recognition Example, Neural Associative Memory, Linear Networks with Minimal Errors, Applications of Neural Network                 (10)