BE Automobile Engineering Semester 8 (BE Fourth Year)University of Mumbai
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Artificial Intelligence Semester 8 (BE Fourth Year) BE Automobile Engineering University of Mumbai Topics and Syllabus

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CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

Topics with syllabus and resources

100.00 Module 1
101.00 Ai and Internal Representation
  • Artificial Intelligence and the World, Representation in AI, Properties of Internal Representation, The
  • Predicate Calculus Intelligent Agents: Concept of Rational Agent, Structure of Intelligent agents, Agent Environments.
  • Problem Solving : Solving problems by searching, Problem Formulation, Search Strategies, Uninformed Search Techniques, DFS, BFS, Uniform cost search, Iterative Deepening, Comparing different Techniques, Informed search methods – Best First Search, heuristic functions, Hill Climbing, A*.IDA*. Crypt Arithmetic, Bactracking for CSP
200.00 Module 2
201.00 Programming in Lisp Or Prolog
  • Lisps, Typing at Lisp,
  • Defining Programs,
  • Basic Flow of Control in Lisp,
  • Lisp Style,
  • Atoms and Lists,
  • Basic Debugging,
  • Building Up List Structure,
  • More on Predicates,
  • Properties, Pointers,
  • Cell Notation and the Internals (Almost) of Lisp,
  • Destructive Modification of Lists,
  • The for Function,
  • Recursion,
  • Scope of Variables Input/Output, Macros
300.00 Module 3
301.00 Fundamentals Concepts and Models of Artificial Neural Systems
  • Biological Neuron and their Artificial Models, Models of ANN, Learning and Adaptation, Neural Networking Learning Rules.
  • Single-layer Perception Classifiers Multilayer Feed forward Networks : Linearly Nonseparable Pattern Classification, Delta Learning Rule, Feed forward Recall and Error BackPropagation Training, Learning Factor
400.00 Module 4
401.00 Fuzzy Systems
  • Fuzzy Sets: Fuzzy Relations, Fuzzy Function, Fuzzy Measures, probabilities possibilities.
  • Fuzzy Modeling and applications of Fuzzy Control. Neural and fuzzy machine Intelligence
500.00 Module 5
501.00 Generic Algorithm
  • Simple generic algorithm,
  • Simulation by hands,
  • similarity templates (Schemata),
  • Mathematical foundations,
  • Schema processing at work,
  • Two armed and k armed Bandit Problem,
  • Building blocks hypothesis,
  • Minimal Deceptive Problem,
  • Computer implementation of generic algorithm,
  • Data structures, Reproduction, Cross over and mutation.
  • Time to response and time to cross mapping objective function to fitness from fitness scaling.
  • Application of generic algorithm.
  • De Jong and Function Optimization.
  • Improvement in basic techniques, Improvement to genetics based machine learning, application of genetic based machine learning
600.00 Module 6
601.00 Data Mining and Information Retrieval
  • Data warehousing & Data Mining.
  • Online Analytic Processing [OLAP]: its architecture and its use.
  • Java implementations, classification trees and exploratory data analysis [EDA].
  • EDA Vs Hypothesis Testing, Computational EDA Techniques, Graphical [Data Visualization], EDA techniques for function fitting, data smoothing, layering, tessellations, contour projections, Verification of results of EDA. Applications & trends in data mining.
  • Case Studies

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