BSCS 6thSemester Course Breakdown (CS): Artifical Intelligence Credit Hours: 3(2-2) Course Code: CS-632 Department of Computer Science Course Objectives: On completion of this module, students should be able to: • Demonstrate an understanding of the fundamental ideas of problem solving using AI. • Demonstrate an understanding of the fundamental ideas of Knowledge Representation and Reasoning. • Demonstrate an understanding of the principles of a number of different approaches to machine learning. • Demonstrate the ability to apply AI and Computational Intelligence techniques to a variety of research and application projects. Course Contents: This course serves as an introduction to the techniques and applications of artificial intelligence (AI) including a study of intelligent agents, search techniques, logical agents, knowledge representation and reasoning formalisms, learning paradigms, Expert systems and some of machine learning techniques like neural networks, genetic algorithms, Fuzzy logic, decision trees etc. Recommended Books: Text: • S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Pearson Education Series in AI Reference: • Luger, George & Stubblefield, William, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th ed.), • Nils J Nilson, Artificial Intelligence – A New Synthesis, Morgan Kaufman Publishers, Elsevier, USA. • Introduction to PROLOG by P. Brna. • Patrick Henry Winston, Artificial Intelligence, Third Edition, Pearson Eucation Series in AI. • Ivan Bratko, PROLOG – Programming for AI, Third Edition, Pearson Education Series in AI. Grading Policy: There will be an evaluation from theory and practical separately. Students are required to qualify both (theory & practical) independently to pass the course. Total Subject Marks: 60 Theory Marks: 40 Quizzes/Assignments : 08 marks Mid Term : 12 marks Final Term (Theory) : 20 marks Practical Marks: 20 Lab Reports : 10 marks Final Project : 10 marks Quizzes and Homework Assignments (HAs): 05 quizzes and 04 HAs shall be given for a 02-2 credit-hours course, evenly distributed over the length of a semester. The quizzes shall be unannounced and of 10 minutes duration on the average. The graded quizzes shall be returned in the next lecture and the graded HAs after a week. The HAs shall be solved independently by all students. Plagiarism is not 1 BSCS 6th Semester Course Breakdown (CS): Artifical Intelligence Credit Hours: 3(2-2) Course Code: CS-632 Department of Computer Science allowed; this will result in cancellation of the HA, in addition to reporting the matter to the management for appropriate action. Mid Semester Examination (MSE) and End Semester Examination (ESE): MSE and ESE will be given in the middle and at the end of a semester, respectively, as instructed by the Examination Branch (Ex Br). Attendance Policy: 75 % attendance of students is mandatory for eligibility to appear in the ESE. Students’ Hours Monday: 1:00 pm – 2:00 pm Wednesday: 9:00 am – 10:00 pm Lecture Breakdown Lecture No. Week 1 Lect. 1 Lab. 1 Week 2 Lect. 2 Lab. 2 Week 3 Lect. 3 Topics Introduction to AI Definition of AI Typical AI problems Practical Impact of AI Approaches to AI Limits of AI Today AI History Introduction to Agent + Lab Excercise Agent Environment Agent architectures Problem Solving using Search Introduction to State Space Search State space search Examples Explicit vs Implicit state space Uninformed Search + Lab Excercise Search Strategies Breadth-first, Depth-first, Bidirectional, Iterative-Deepening Search etc Reasoning Case Study Informed Search Strategies Introduction Best First Search Hill climbing 2 BSCS 6th Semester Lab. 3 Week 4 Lect. 4 Lab. 4 Week 5 Lect. 5 Lab. 5 Week 6 Lect. 6 Lab. 6 Week 7 Lect. 7 Lab. 7 Course Breakdown (CS): Artifical Intelligence Credit Hours: 3(2-2) Course Code: CS-632 Department of Computer Science Informed Search Strategies-cont’d + Lab Excercise Branch and Bound Beam search A* Search Game Playing Adversarial search The min max algorithm Alpha-Beta Pruning Constraint satisfaction problems + Lab Excercise Constraint Satisfaction Problems Representation of CSP Solving CSPs Constraint satisfaction problems Variable and Value Ordering Heuristic Search in CSP Knowledge Representation and Logic + Lab Exercise Propositional Logic Knowledge Representation and Reasoning Propositional Logic Propositional Logic Inference Propositional Logic inference rules Rules of Inference Using Inference Rules to Prove a Query/Goal/Theorem Soundness and Completeness Knowledge Representation and Logic + Lab Exercise First Order Logic(FOL) First Order Logic Unification Semantics First Order Logic – cont’d Herbrand Universe Deduction Soundness, Completeness, Consistency, Satisfiability Inference in FOL + Lab Excercise Resolution Resolution in First Order Logic 3 BSCS 6th Semester Course Breakdown (CS): Artifical Intelligence Credit Hours: 3(2-2) Course Code: CS-632 Week 8-9 Mid Term Exam Week 10 Lect. 8 Lab. 8 Week 11 Lect. 9 Lab. 9 Week 12 Lect. 10 Lab. 10 Week 13 Lect. 11 Lab. 11 Week 14 Department of Computer Science Lect. 12 Inference in FOL – cont’d Proof as Search Some Proof Strategies Non-Monotonic Reasoning Knowledge Representation and Logic (Rule based Systems) + Lab Exercise Rule based Systems Rule Based Systems Horn Clause Logic Backward Chaining Forward chaining Rule based Systems – cont’d Programs in PROLOG Expert Systems Reasoning with Uncertainty - Probabilistic reasoning + Lab Excercise Reasoning with Uncertain information Probabilistic Reasoning Review of Probability Theory 22 Probabilistic Inference Probabilistic Inference Rules Bayes Networks Bayesian Networks Semantics of Bayesian Networks Learning of Bayesian Network Parameters A Basic Idea of Inferencing with Bayes Networks + Lab Excercise Inferencing in Bayesian Networks Approximate Inferencing in Bayesian Networks Reasoning with uncertainty-Fuzzy Reasoning Reasoning with Uncertainty The Problem, Vagueness, Fuzziness Fuzzy Set Representation - Fuzzy Sets: Basic Concepts Fuzzy Reasoning Fuzzy Inferencing APPLICATIONS Lab Excercise Machine Learning Introduction 4 BSCS 6th Semester Course Breakdown Lab. 12 Week 15 Lect. 13 Lab. 13 Week 16 Lect. 14 Lab. 14 Week 17 (CS): Artifical Intelligence Credit Hours: 3(2-2) Course Code: CS-632 Department of Computer Science Introduction to Learning Taxonomy of Learning Systems Mathematical formulation of the inductive learning problems Learning From Observations Concept Learning Rule Induction and Decision Tree - I + Lab Excercise Decision Trees Rule Induction and Decision Trees Splitting Functions Decision Tree Pruning Learning and Neural Networks Neural Networks – an intro Perceptron o Perceptron Learning o The Perceptron Rule o The Delta Rule Neural Networks – Cont’d + Lab Excercise Multi-Layer Perceptrons o Back-Propagation Algorithm o Forward Propagation, Backward Propagation Natural Language Processing Natural Language Processing (NLP) o Models to represent Linguistic Knowledge o Overview of Algorithms to Manipulate Linguistic Knowledge Natural Language Processing + Lab Excercise Parsing Knowledge Representation for NLP Applications of Natural Language Processing Machine Translation Lect. 15 Project Presentations Lab. 15 Project Presentations Week 18-19 Final Term 5