Email:info@araniconsulting.com

Part – A: Artificial Intelligence

Unit 1: Artificial Intelligence and Predicate Calculus

  1. Introduction
  2. AI Roots and Applications
  3. Propositional Calculus
  4. Predicate Calculus

Unit 2: AI Programming Languages

  1. Prolog and Lisp
  2. Syntax
  3. Data Types
  4. Control Mechanisms

Unit 3: Graph Theory and Strategies for State Space Searches

  1. Graph Theory & Finite State Machine
  2. State Space Search Algorithms
  3. Reasoning Strategies

Unit 4: Heuristic Search Algorithms

  1. Heuristic Search Issues
  2. Heuristic Search Applications
  3. Hill Climbing Algorithm
  4. Dynamic Programming
  5. Best-first Search

Unit 5: Control and Implementation of State Space Searches

  1. Related Issues
  2. Recursion-based Searching
  3. Production and Blackboard Systems Architecture

Part – B: Artificial Intelligence

Unit 1: Introduction to Machine Learning

  1. Introduction to Big Data and Machine Learning

Unit 2: Walking with Python or R

  1. Understanding Python or R

Unit 3: Machine Learning Techniques

  1. Types of Learning
  2. Advice for Applying Machine Learning
  3. Machine Learning System Design

Unit 4: Supervised Learning

  1. Regression
  2. Classification

Unit 5: Supervised Learning: Regression

  1. Predicting House Prices: A Case Study in Regression

Unit 6: Supervised Learning – Clustering

  1. Analyzing the Sentiment of Reviews: A Case Study in Classification

Unit 7: Unsupervised Learning

  1. Clustering
  2. Recommendation
  3. Deep Learning

Unit 8: Unsupervised Learning Clustering

  1. Document Retrieval: A Case Study in Clustering and Measuring Similarity

Unit 9: Unsupervised Recommendation

  1. Recommending Products

Unit 10: Unsupervised Deep Learningb

  1. Deep Learning: Searching for Images

Email:info@araniconsulting.com

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