Final Updated(18.12.2018) -17CS2212-AI (2018_12_25 05_40_40 UTC).pdf

Final Updated(18.12.2018) -17CS2212-AI (2018_12_25 05_40_40 UTC).pdf

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Course Handout Template for Y17 Admitted Batches 1 K L Deemed to be University Department of Computer Science & Engineering Course Handout A.Y.2018-19, Even Semester Course Title : Artificial Intelligence Course Code : 17CS2212 L-T-P-S Structure : 2-0-2-6 Credits : 4.5 Pre-requisite : Discrete Mathematics Course Coordinator : Dr. Anjali Mathur Team of Instructors : Dr. V Chandra Prakash, Dr. P. Rajeshwari , Dr. S. Srinivasa Rao, Dr. P. Vidhyulatha, Dr. Vijayasri, Dr. G. Sambashivam, Mrs. Lakshmi Prasanna, Mr. David Raju, Mr. Yellaswamy Teaching Associates : - Course Objective : The emphasis of the course is on understanding the various search algorithms that are useful to solve AI problems, knowledge representation schemes, Game Playing and Probability and uncertainty techniques and to solving AI problems using PYTHON language. Course Rationale : With the usage of Internet and World Wide Web increasing day by day, the field of AI and its techniques are being used in many areas such as machine learning, which directly affect human life. Various techniques for encoding knowledge in computer systems such as predicate logic, production rules, and semantic networks find application in real world problems. The fields of AI such as Game Playing and Probability and uncertainty techniques are also important. This course provides the basic concepts of Artificial Intelligence which is essential for the student to understand the advanced courses like Machine Learning, Natural Language Processing, Soft Computing, Data Mining, IoT, Big Data Analytics and so on. In PYTHON, is one of the most useful languages to solve AI problems? Therefore, it is a pretty useful language for the sake of AI. COURSE OUTCOMES (COs): CO No Course Outcome (CO) PO/PSO Blooms Taxonomy Level (BTL) CO1 Understand the problem, well defined problems and their solutions, uninformed search PO1, PO2 2 CO2 Local Search, Game playing with adversarial search, Constraint Satisfaction problem PO2, PO4,PO6,PO7 3 CO3 Building Knowledge and reasoning:- Propositional logics, first order logic, forward and backward reasoning, resolution PO4, PO2,PO6,PO7 4 CO4 Analyzing uncertainty using Baye’s theorem, Hidden Markov model and Kalman Filters. PO4, PO2,PO6,PO7, PSO1 4
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Course Handout Template for Y17 Admitted Batches 2 CO5 (Only for lab components) Solving AI problems using Python PO2, PO4 5 COURSE OUTCOME INDICATORS (COIs): Course Outcome No. Highest BTL COI-1 (BTL1) COI-2 (BTL2) COI-3 (BTL3) COI-4 (BTL4) COI-5 (BTL5) COI-6 (BTL3) CO 1 2 Summarized the Foundations of AI ,Agents and Structure of Agents Illustrate Uninformed & informed Search Strategies. CO 2 3 Building the Concepts of Local Search &Games Build General Game Tree in Game Playing. Compare and contrast Min Max Algorithms, alpha Beta Pruning Analyse the Concept of Constrain Satisfactio n CO 3 4 Outline about the Knowledge Based Agents , Propositiona l theorem proving Analyzing knowledge representatio n using predicate logic Analyse Analyse the syntax and Semantics of First order logic.
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  • Spring '19
  • Dr. Anjali Mathur
  • Search algorithms, Search algorithm, A* search algorithm, Constraint satisfaction problem, course handout

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