M.TECH IN COMPUTER SCIENCE AND ENGINEERING (SCS) Choice Based Credit System (CBCS) and Outcome Based Education (OBE) SEMESTER –I | ||||||
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | ||||||
Course Code | 20SCS12, 20SSE254, 20SAM12, 20SIS31 | CIE Marks | 40 | |||
Teaching Hours/Week (L:P:S) | 3:0:2 | SEE Marks | 60 | |||
Credits | 04 | Exam Hours | 03 | |||
Module-1 : Introduction, problem Solving: state space search and control strategies | ||||||
Module-2: Problem reduction and Game playing, Logic concepts and logic programming | ||||||
Module-3: Advanced problem-solving paradigm: planning Knowledge representation | ||||||
Module-4 :Uncertainty Measure: Probability Theory, Bayesian Belief Networks, Machine Learning Paradigms: Machine learning system, supervised and unsupervised learnings, Inductive, deductive learning, Clustering | ||||||
Module-5: Support vector Machine, case-based reasoning and learning. ANN: Single Layer, Multilayer. RBF, Design issues in ANN, Recurrent Network | ||||||
Course outcomes: At the end of the course the student will be able to: | ||||||
● Define Artificial intelligence and identify problems for AI. Characterize the search techniques to solve problems and recognize the scope of classical search techniques ● Define knowledge and its role in AI. Demonstrate the use of Logic in solving AI problems ● Demonstrate handling of uncertain knowledge and reasoning in probability theory. ● Understanding of Learning methods | ||||||
Question paper pattern: The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60. ● The question paper will have ten full questions carrying equal marks. ● Each full question is for 20 marks. ● There will be two full questions (with a maximum of four sub questions) from each module. ● Each full question will have sub question covering all the topics under a module. ● The students will have to answer five full questions, selecting one full question from each module. | ||||||
Textbook/ Textbooks | ||||||
Sl No | Title of the book | Name of the Author/s | Publisher Name | Edition and year | ||
1 | Artificial Intelligence: | SarojKaushik | Cengage Learning | 2014 Edition |
Reference Books | ||||
1 | Artificial Intelligence: Structures and Strategies for Complex Problem Solving | George F Luger | Pearson Addison Wesley | 6th Ed, 2008 |
2 | Artificial Intelligence | E Rich, K Knight, and S B Nair | Tata Mc-Graw Hill | 3rd Ed, 2009 |
3 | Artificial Intelligence: A Modern Approach | Stuart Russell and Peter Norvig | Prentice Hall | 3rd, 2009 |
VTU QP JULY 2021: https://drive.google.com/file/d/1dUrNOsjWoaYyLcjhl5WxXWBQ0a7s6fOd/view?usp=sharing
Blog: https://lilianweng.github.io/lil-log/
| S.No. | Roll No. | Admn.No. | Student Name | Course | AI-ML Project Title |
| 1 | CSEMMTECH/07/202 1 | 5584 | ANGELEN ELIZABITH . | MTECH | |
| 2 | CSEM.TECH/02/2021 | 5337 | JAYA DIXIT . | MTECH | |
| 3 | CSEMTECHG/02/202 1 | 5707 | PAWAR ARATHI . | MTECH | |
| 4 | CSEMTECHM/12/202 1 | 5736 | RAHILA FATIMA . | MTECH | |
| 5 | MTECHCSEG/01/202 1 | 5701 | AISHWARYA . | MTECH | |
| 6 | MTECHCSEG/03/202 1 | 5713 | HUMERA TAHSEEN . | MTECH | |
| 7 | MTECHCSEM/13/202 1 | 5729 | ANCHAL . | MTECH | |
| 8 | MTECHCSM/01/2021 | 5323 | VIJAYLAXMI . | MTECH | |
| 9 | MTECHCSM/04/2021 | 5434 | SHEELVANT GEETA | MTECH | |
| 10 | MTECHCSM/05/2021 | 5582 | SYEDA TUBA MAHVESH . | MTECH | |
| 11 | MTECHCSM/11/2021 | 5728 | FARZANA KHANAM . | MTECH | |
| 12 | M.TECHSCM/06/2021 | 5581 | SAIMA DANISH . | MTECH | |
| 13 | PGCSM/08/2021-22 | 5686 | MAHESHWARI . | MTECH | |
| 14 | PGCSM/09/2021-22 | 5687 | BI BI FATOMA . | MTECH | |
| 15 | PGCSM/10/2021-22 | 5688 | AZRA FARHEEN . | MTECH | |
| Note: | 1. All PG Students must participate in above activity | ||||
2. Make sure the project not repeated/downloaded from web | |||||
3. The presentation of Each project must be done regularly |
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