Category: Nptel Assignment Answers 2024
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NPTEL Assignment Answers and Solutions 2024 (July-Dec). Get Answers of Week 1 2 3 4 5 6 7 8 8 10 11 12 for all courses. This guide offers clear and accurate answers for your all assignments across various NPTEL courses
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Nptel assignment answers 2024 with solutions (july-dec), how to use this repo to see nptel assignment answers and solutions 2024.
If you're here to find answers for specific NPTEL courses, follow these steps:
Access the Course Folder:
- Navigate to the folder of the course you are interested in. Each course has its own folder named accordingly, such as cloud-computing or computer-architecture .
Locate the Weekly Assignment Files:
- Inside the course folder, you will find files named week-01.md , week-02.md , and so on up to week-12.md . These files contain the assignment answers for each respective week.
Select the Week File:
- Click on the file corresponding to the week you are interested in. For example, if you need answers for Week 3, open the week-03.md file.
Review the Answers:
- Each week-XX.md file provides detailed solutions and explanations for that week’s assignments. Review these files to find the information you need.
By following these steps, you can easily locate and use the assignment answers and solutions for the NPTEL courses provided in this repository. We hope this resource assists you in your studies!
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Here's a list of courses currently available in this repository:
- Artificial Intelligence Search Methods for Problem Solving
- Cloud Computing
- Computer Architecture
- Cyber Security and Privacy
- Data Science for Engineers
- Data Structure and Algorithms Using Java
- Database Management System
- Deep Learning for Computer Vision
- Deep Learning IIT Ropar
- Digital Circuits
- Ethical Hacking
- Introduction to Industry 4.0 and Industrial IoT
- Introduction to Internet of Things
- Introduction to Machine Learning IIT KGP
- Introduction to Machine Learning
- Introduction to Operating Systems
- ML and Deep Learning Fundamentals and Applications
- Problem Solving Through Programming in C
- Programming DSA Using Python
- Programming in Java
- Programming in Modern C
- Python for Data Science
- Soft Skill Development
- Soft Skills
- Software Engineering
- Software Testing
- The Joy of Computation Using Python
- Theory of Computation
Note: This repository is intended for educational purposes only. Please use the provided answers as a guide to better understand the course material.
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NPTEL An Introduction to Artificial Intelligence Assignment 1 Answers 2023
NPTEL An Introduction to Artificial Intelligence Assignment 1 Answers 2023 :- If you are searching for answers of nptel then you are at the right place. we will provide the answers of NPTEL An Introduction to Artificial Intelligence Assignment 1 Answers 2023.
1. Which computer system beat Gary Kasparov in a chess game in 1997? a. Shakey b. Deep Thought c. Deep Blue d. STRIPS
2. Which of the following was a major achievement of the computer EQP in 1996? a. Proved a mathematical theorem b. Defeated a world champion in a board game c. Demonstrated human-level language translation d. Performed human-level object recognition
3. Which of the following is NOT the reason for the recent takeoff of Deep learning? a. It can be used to model all real-world problems b. Increased computational power with the advent of GPUs c. Numerical optimizations and algorithmic efficiency d. Access to a lot more labeled data
4. The Turing test considers which of the following trait as evidence of machine intelligence. a. Acting humanly b. Thinking humanly c. Acting rationally d. Thinking rationally
5. The DARPA Urban Challenge was a competition involving building AI agents for a. Automated gameplay b. Recognizing objects on urban streets c. Self-driving cars d. Machine translation
6. Which of the following actions is a rational agent expected to prioritize? a. The morally correct action b. Action which leads to the greatest reward c. Action which leads to greatest expected loss d. Socially acceptable action
7. Which statements among the following statements is/are true for AlphaGo? a. It was developed by researchers at Deep Mind b. It defeated the world champion in the game of chess c. It is an example of a weak AI agent d. None of the above
8. Which of the following is a perceptual task? a. Proving mathematical theorems b. Playing chess c. Medical diagnosis d. Object recognition
9. Which of the following are shortcomings of Logic-based AI systems that led to probability-based AI systems? a. Logic-based AI systems could not run on conventional general-purpose computer hardware b. Logic cannot be used to make deductions from observations c. Logic cannot be used to take into account the uncertainty in the environment d. Logic is NP-hard
10. Choose All correct statements about knowledge-based strong AI systems a. They are supposed to perform general-purpose problem-solving b. Knowledge about some specific domain is provided to such systems c, The goal of such systems is to achieve human-level performance on one specific task d. A* algorithm is an example of a strong AI system
About This Course
An Introduction to Artificial Intelligence by IIT Delhi course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.
CRITERIA TO GET A CERTIFICATE
Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100
Final score = Average assignment score + Exam score
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
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the week 1 assignment is given below= 1=c; 2=a; 3=c 4=c; 5=c; 6=b; 7=b; 8=d; 9=d; 10=b.
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NPTEL An Introduction to Artificial Intelligence Assignment 3 Answers
NPTEL An Introduction to Artificial Intelligence Assignment 3 Answers 2022:- All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge.
What is An Introduction to Artificial Intelligence?
An Introduction to Artificial Intelligence by IIT Delhi course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.
CRITERIA TO GET A CERTIFICATE
Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100
Final score = Average assignment score + Exam score
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
| Assignment 1 | |
| Assignment 2 | |
| Assignment 3 | |
| Assignment 4 | |
| Assignment 5 | NA |
| Assignment 6 | NA |
| Assignment 7 | NA |
| Assignment 8 | NA |
| Assignment 9 | NA |
| Assignment 10 | NA |
| Assignment 11 | NA |
| Assignment 12 | NA |
NPTEL An Introduction to Artificial Intelligence Assignment 3 Answers 2022:-
Q1. If h1 and h2 are two admissible heuristics, then which of the following are guaranteed to be admissible?
(A) max(h1,h2) (B) min(h1,h2) + 1 (C) sqrt(h1*h2) (D) h1 + h2
Answer:- (A), & (C)
👇 FOR NEXT WEEK ASSIGNMENT ANSWERS 👇
Q2. Which of the following evaluation functions would emulate the behavior of greedy best-first search?
(A) f(n) = g(n) (B) f(n) = h(n) (C) f(n) = g(n) + h(n) (D) f(n) = 2h(n)
Answer:- (B) & (D)
Q3. Which of the following graph search algorithms are guaranteed to be complete and optimal? (Assume positive edge costs)
(A) Breadth-first search (B) A* with zero heuristic (C) Uniform Cost Search (D) A* with admissible heuristic
Answer:- (B) & (C)
Q4. Consider the following directed graph,having A as the starting node and G as the goal node, with edge costs as mentioned, and the heuristic values for the nodes are given as – {h(A)=7, h(B)=6, h(C)=5, h(D)=4, h(E)=3, h(F)=3, h(G)=0}:
Which of the following options are correct?
(A) Given heuristic function is not admissible (B) Given heuristic function is admissible (C) Given heuristic function is not consistent (D) Given heuristic function is consistent
Q5. With A as the starting node and G as the goal node, we run the IDA* graph search with depths 1,2,3.. . Assume we stop as soon as the goal node is reached. At what depth will we reach the goal node? (Assume A is at depth 0)
Q6. With A as the starting node and G as the goal node, we run the IDA* graph search with depths 1,2,3.. . Assume we stop as soon as the goal node is reached. What is the optimal path from A to G predicted by IDA*? (Assume A is at depth 0)
Answer:- ADEG
👇FOR NEXT WEEK ASSIGNMENT ANSWERS👇
Pacman is a famous game in which the agent(Pacman) is controlled by the keyboard keys and the goal is to eat the maximum amount of food whilst avoiding being eaten by ghosts. Consider a version of Pacman in which there are no ghosts at all. In any state, Pacman can move left, right, up, or down with a cost of 1. Assume that Pacman can’t move into a wall. The goal of Pacman is to eat all the food in the maze(in any order). We wish to compute admissible heuristics for this problem by relaxing the domain. Consider the following relaxation: Pacman can now “teleport” from any cell to any other cell with a cost of 1 What is the cost of the optimal solution in the relaxed domain?
(A) Length of the shortest path through all the food in the maze (B) Maximum of the length of the shortest path to all food items from the current location. (C) Number of food items in the maze. (D) None of these
Answer:- (C) Number of food items in the maze.
Q8. Is the cost of the optimal solution an admissible heuristic in the original domain? Why?
(A) No (B) Yes, because all solutions of the relaxed domain are solutions in the original domain (C) Yes, because all solutions of the original domain are solutions of the relaxed domain.
Answer:- (C) Yes, because all solutions of the original domain are solutions of the relaxed domain
Q9. Consider the graph below. A is the starting node and B is the goal state. We perform a depth first search branch and bound algorithm (assume zero heuristic) with upper bound cost 6.5. If there are any ties, then the algorithm chooses to expand the lexicographically smaller node. Also, we stop as soon as we reach the goal. What is the first path predicted by the algorithm?
Answer:- AECB
Q10. Consider the graph below. A is the starting node and B is the goal state. We perform a depth first search branch and bound algorithm (assume zero heuristic) with upper bound cost 6.5. If there are any ties, then the algorithm chooses to expand the lexicographically smaller node. Also, we stop as soon as we reach the goal. Now, suppose I change my upper bound to 7.5. What will be the path predicted in this case?
Answer:- AB
Q11. Are these true or false?
(i). All admissible heuristics are consistent. (ii). All consistent heuristics are admissible.
(A) False, False (B) False, True (C) True, False (D) True, True
Answer:- (B) False, True
Q12. Which of the following will return an optimal solution in case the search graph has negative edge-costs?
(A) Uniform cost search (B) Graph search A* with consistent heuristic (C) Both of these (D) None of these
Answer:- (D) None of these
Q13. Consider the graph shown below and answer the next three questions based on it. In the graph, all nodes represent a state. A is the start state, and G is the goal state. The value of the heuristic function for each node is mentioned alongside. For example, the value of the heuristic at E is 1, and at C is 4, etc. Similarly the cost of transitioning from a state to another state is mentioned on the edges. For example, the cost of transitioning from B to D is 3 points.
Write down the order of nodes visited by A* graph search algorithm?
(A) A, C, D, E, G (B) A, B, C, D, F, E, G (C) A, B, D, F, C, E, G (D) A, B, D, F, E, G
Answer:- (C) A, B, D, F, C, E, G
Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.
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- Computer Science and Engineering
- NOC:An Introduction to Artificial Intelligence (Video)
- Co-ordinated by : IIT Delhi
- Available from : 2019-11-13
- Intro Video
- Introduction: What to Expect from AI
- Introduction: History of AI from 40s - 90s
- Introduction: History of AI in the 90s
- Introduction: History of AI in NASA & DARPA(2000s)
- Introduction: The Present State of AI
- Introduction: Definition of AI Dictionary Meaning
- Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally
- Introduction: Definition of AI Rational Agent View of AI
- Introduction: Examples Tasks, Phases of AI & Course Plan
- Uniform Search: Notion of a State
- Uniformed Search: Search Problem and Examples Part-2
- Uniformed Search: Basic Search Strategies Part-3
- Uniformed Search: Iterative Deepening DFS Part-4
- Uniformed Search: Bidirectional Search Part-5
- Informed Search: Best First Search Part-1
- Informed Search: Greedy Best First Search and A* Search Part-2
- Informed Search: Analysis of A* Algorithm Part-3
- Informed Search Proof of optimality of A* Part-4
- Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5
- Informed Search: Admissible Heuristics and Domain Relaxation Part-6
- Informed Search: Pattern Database Heuristics Part-7
- Local Search: Satisfaction Vs Optimization Part-1
- Local Search: The Example of N-Queens Part-2
- Local Search: Hill Climbing Part-3
- Local Search: Drawbacks of Hill Climbing Part-4
- Local Search: of Hill Climbing With random Walk & Random Restart Part-5
- Local Search: Hill Climbing With Simulated Anealing Part-6
- Local Search: Local Beam Search and Genetic Algorithms Part-7
- Adversarial Search : Minimax Algorithm for two player games
- Adversarial Search : An Example of Minimax Search
- Adversarial Search : Alpha Beta Pruning
- Adversarial Search : Analysis of Alpha Beta Pruning
- Adversarial Search : Analysis of Alpha Beta Pruning (contd...)
- Adversarial Search : Horizon Effect, Game Databases & Other Ideas
- Adversarial Search: Summary and Other Games
- Constraint Satisfaction Problems: Representation of the atomic state
- Constraint Satisfaction Problems: Map coloring and other examples of CSP
- Constraint Satisfaction Problems: Backtracking Search
- Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search
- Constraint Satisfaction Problems: Inference for detecting failures early
- Constraint Satisfaction Problems: Exploiting problem structure
- Logic in AI : Different Knowledge Representation systems - Part 1
- Logic in AI : Syntax - Part - 2
- Logic in AI : Semantics - Part - 3
- Logic in AI : Forward Chaining - Part 4
- Logic in AI : Resolution - Part - 5
- Logic in AI : Reduction to Satisfiability Problems - Part - 6
- Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7
- Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8
- Uncertainty in AI: Motivation
- Uncertainty in AI: Basics of Probability
- Uncertainty in AI: Conditional Independence & Bayes Rule
- Bayesian Networks: Syntax
- Bayesian Networks: Factoriziation
- Bayesian Networks: Conditional Independences and d-Separation
- Bayesian Networks: Inference using Variable Elimination
- Bayesian Networks: Reducing 3-SAT to Bayes Net
- Bayesian Networks: Rejection Sampling
- Bayesian Networks: Likelihood Weighting
- Bayesian Networks: MCMC with Gibbs Sampling
- Bayesian Networks: Maximum Likelihood Learning"
- Bayesian Networks: Maximum a-Posteriori LearningÃÂ
- Bayesian Networks: Bayesian Learning
- Bayesian Networks: Structure Learning and Expectation Maximization
- Introduction, Part 10: Agents and Environments
- Decision Theory: Steps in Decision Theory
- Decision Theory: Non Deterministic Uncertainty
- Probabilistic Uncertainty & Value of perfect information
- Expected Utility vs Expected Value
- Markov Decision Processes: Definition
- Markov Decision Processes: An example of a Policy
- Markov Decision Processes: Policy Evaluation using system of linear equations
- Markov Decision Processes: Iterative Policy Evaluation
- Markov Decision Processes: Value Iteration
- Markov Decision Processes: Policy Iteration and Applications & Extensions of MDPs
- Reinforcement Learning: Background
- Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning)
- Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning)
- Reinforcement Learning: TD Learning
- Reinforcement Learning: TD Learning and Computational Neuroscience
- Reinforcement Learning: Q Learning
- Reinforcement Learning: Exploration vs Exploitation Tradeoff
- Reinforcement Learning: Generalization in RL
- Deep Learning : Perceptrons and Activation functions
- Deep Learning : Example of Handwritten digit recognition
- Deep Learning : Neural Layer as matrix operations
- Deep Learning : Differentiable loss function
- Deep Learning : Backpropagation through a computational graph
- Deep Learning : Thin Deep Vs Fat Shallow Networks
- Deep Learning : Convolutional Neural Networks
- Deep Learning : Deep Reinforcement Learning
- Ethics of AI : Humans vs Robots
- Ethics of AI : Robustness and Transparency of AI systems
- Ethics of AI : Data Bias and Fairness of AI systems
- Ethics of AI : Accountability, privacy and Human-AI interaction
- Watch on YouTube
- Assignments
- Download Videos
- Transcripts
| Module Name | Download |
|---|---|
| noc20_cs42_assigment_1 | |
| noc20_cs42_assigment_10 | |
| noc20_cs42_assigment_11 | |
| noc20_cs42_assigment_12 | |
| noc20_cs42_assigment_13 | |
| noc20_cs42_assigment_2 | |
| noc20_cs42_assigment_3 | |
| noc20_cs42_assigment_4 | |
| noc20_cs42_assigment_5 | |
| noc20_cs42_assigment_6 | |
| noc20_cs42_assigment_7 | |
| noc20_cs42_assigment_8 | |
| noc20_cs42_assigment_9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction: What to Expect from AI | |
| 2 | Introduction: History of AI from 40s - 90s | |
| 3 | Introduction: History of AI in the 90s | |
| 4 | Introduction: History of AI in NASA & DARPA(2000s) | |
| 5 | Introduction: The Present State of AI | |
| 6 | Introduction: Definition of AI Dictionary Meaning | |
| 7 | Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally | |
| 8 | Introduction: Definition of AI Rational Agent View of AI | |
| 9 | Introduction: Examples Tasks, Phases of AI & Course Plan | |
| 10 | Uniform Search: Notion of a State | |
| 11 | Uniformed Search: Search Problem and Examples Part-2 | |
| 12 | Uniformed Search: Basic Search Strategies Part-3 | |
| 13 | Uniformed Search: Iterative Deepening DFS Part-4 | |
| 14 | Uniformed Search: Bidirectional Search Part-5 | |
| 15 | Informed Search: Best First Search Part-1 | |
| 16 | Informed Search: Greedy Best First Search and A* Search Part-2 | |
| 17 | Informed Search: Analysis of A* Algorithm Part-3 | |
| 18 | Informed Search Proof of optimality of A* Part-4 | |
| 19 | Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5 | |
| 20 | Informed Search: Admissible Heuristics and Domain Relaxation Part-6 | |
| 21 | Informed Search: Pattern Database Heuristics Part-7 | |
| 22 | Local Search: Satisfaction Vs Optimization Part-1 | |
| 23 | Local Search: The Example of N-Queens Part-2 | |
| 24 | Local Search: Hill Climbing Part-3 | |
| 25 | Local Search: Drawbacks of Hill Climbing Part-4 | |
| 26 | Local Search: of Hill Climbing With random Walk & Random Restart Part-5 | |
| 27 | Local Search: Hill Climbing With Simulated Anealing Part-6 | |
| 28 | Local Search: Local Beam Search and Genetic Algorithms Part-7 | |
| 29 | Adversarial Search : Minimax Algorithm for two player games | |
| 30 | Adversarial Search : An Example of Minimax Search | |
| 31 | Adversarial Search : Alpha Beta Pruning | |
| 32 | Adversarial Search : Analysis of Alpha Beta Pruning | |
| 33 | Adversarial Search : Analysis of Alpha Beta Pruning (contd...) | |
| 34 | Adversarial Search : Horizon Effect, Game Databases & Other Ideas | |
| 35 | Adversarial Search: Summary and Other Games | |
| 36 | Constraint Satisfaction Problems: Representation of the atomic state | |
| 37 | Constraint Satisfaction Problems: Map coloring and other examples of CSP | |
| 38 | Constraint Satisfaction Problems: Backtracking Search | |
| 39 | Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search | |
| 40 | Constraint Satisfaction Problems: Inference for detecting failures early | |
| 41 | Constraint Satisfaction Problems: Exploiting problem structure | |
| 42 | Logic in AI : Different Knowledge Representation systems - Part 1 | |
| 43 | Logic in AI : Syntax - Part - 2 | |
| 44 | Logic in AI : Semantics - Part - 3 | |
| 45 | Logic in AI : Forward Chaining - Part 4 | |
| 46 | Logic in AI : Resolution - Part - 5 | |
| 47 | Logic in AI : Reduction to Satisfiability Problems - Part - 6 | |
| 48 | Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7 | |
| 49 | Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8 | |
| 50 | Uncertainty in AI: Motivation | |
| 51 | Uncertainty in AI: Basics of Probability | |
| 52 | Uncertainty in AI: Conditional Independence & Bayes Rule | |
| 53 | Bayesian Networks: Syntax | |
| 54 | Bayesian Networks: Factoriziation | |
| 55 | Bayesian Networks: Conditional Independences and d-Separation | |
| 56 | Bayesian Networks: Inference using Variable Elimination | |
| 57 | Bayesian Networks: Reducing 3-SAT to Bayes Net | |
| 58 | Bayesian Networks: Rejection Sampling | |
| 59 | Bayesian Networks: Likelihood Weighting | |
| 60 | Bayesian Networks: MCMC with Gibbs Sampling | |
| 61 | Bayesian Networks: Maximum Likelihood Learning" | |
| 62 | Bayesian Networks: Maximum a-Posteriori LearningÃÂ | |
| 63 | Bayesian Networks: Bayesian Learning | |
| 64 | Bayesian Networks: Structure Learning and Expectation Maximization | |
| 65 | Introduction, Part 10: Agents and Environments | |
| 66 | Decision Theory: Steps in Decision Theory | |
| 67 | Decision Theory: Non Deterministic Uncertainty | |
| 68 | Probabilistic Uncertainty & Value of perfect information | |
| 69 | Expected Utility vs Expected Value | |
| 70 | Markov Decision Processes: Definition | |
| 71 | Markov Decision Processes: An example of a Policy | |
| 72 | Markov Decision Processes: Policy Evaluation using system of linear equations | |
| 73 | Markov Decision Processes: Iterative Policy Evaluation | |
| 74 | Markov Decision Processes: Value Iteration | |
| 75 | Markov Decision Processes: Policy Iteration and Applications & Extensions of MDPs | |
| 76 | Reinforcement Learning: Background | |
| 77 | Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning) | |
| 78 | Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning) | |
| 79 | Reinforcement Learning: TD Learning | |
| 80 | Reinforcement Learning: TD Learning and Computational Neuroscience | |
| 81 | Reinforcement Learning: Q Learning | |
| 82 | Reinforcement Learning: Exploration vs Exploitation Tradeoff | |
| 83 | Reinforcement Learning: Generalization in RL | |
| 84 | Deep Learning : Perceptrons and Activation functions | |
| 85 | Deep Learning : Example of Handwritten digit recognition | |
| 86 | Deep Learning : Neural Layer as matrix operations | |
| 87 | Deep Learning : Differentiable loss function | |
| 88 | Deep Learning : Backpropagation through a computational graph | |
| 89 | Deep Learning : Thin Deep Vs Fat Shallow Networks | |
| 90 | Deep Learning : Convolutional Neural Networks | |
| 91 | Deep Learning : Deep Reinforcement Learning | |
| 92 | Ethics of AI : Humans vs Robots | |
| 93 | Ethics of AI : Robustness and Transparency of AI systems | |
| 94 | Ethics of AI : Data Bias and Fairness of AI systems | |
| 95 | Ethics of AI : Accountability, privacy and Human-AI interaction | |
| 96 | Wrapup |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction: What to Expect from AI | |
| 2 | Introduction: History of AI from 40s - 90s | |
| 3 | Introduction: History of AI in the 90s | |
| 4 | Introduction: History of AI in NASA & DARPA(2000s) | |
| 5 | Introduction: The Present State of AI | |
| 6 | Introduction: Definition of AI Dictionary Meaning | |
| 7 | Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally | |
| 8 | Introduction: Definition of AI Rational Agent View of AI | |
| 9 | Introduction: Examples Tasks, Phases of AI & Course Plan | |
| 10 | Uniform Search: Notion of a State | |
| 11 | Uniformed Search: Search Problem and Examples Part-2 | |
| 12 | Uniformed Search: Basic Search Strategies Part-3 | |
| 13 | Uniformed Search: Iterative Deepening DFS Part-4 | |
| 14 | Uniformed Search: Bidirectional Search Part-5 | |
| 15 | Informed Search: Best First Search Part-1 | |
| 16 | Informed Search: Greedy Best First Search and A* Search Part-2 | |
| 17 | Informed Search: Analysis of A* Algorithm Part-3 | |
| 18 | Informed Search Proof of optimality of A* Part-4 | |
| 19 | Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5 | |
| 20 | Informed Search: Admissible Heuristics and Domain Relaxation Part-6 | |
| 21 | Informed Search: Pattern Database Heuristics Part-7 | |
| 22 | Local Search: Satisfaction Vs Optimization Part-1 | |
| 23 | Local Search: The Example of N-Queens Part-2 | |
| 24 | Local Search: Hill Climbing Part-3 | |
| 25 | Local Search: Drawbacks of Hill Climbing Part-4 | |
| 26 | Local Search: of Hill Climbing With random Walk & Random Restart Part-5 | |
| 27 | Local Search: Hill Climbing With Simulated Anealing Part-6 | |
| 28 | Local Search: Local Beam Search and Genetic Algorithms Part-7 | |
| 29 | Adversarial Search : Minimax Algorithm for two player games | |
| 30 | Adversarial Search : An Example of Minimax Search | |
| 31 | Adversarial Search : Alpha Beta Pruning | |
| 32 | Adversarial Search : Analysis of Alpha Beta Pruning | |
| 33 | Adversarial Search : Analysis of Alpha Beta Pruning (contd...) | |
| 34 | Adversarial Search : Horizon Effect, Game Databases & Other Ideas | |
| 35 | Adversarial Search: Summary and Other Games | |
| 36 | Constraint Satisfaction Problems: Representation of the atomic state | |
| 37 | Constraint Satisfaction Problems: Map coloring and other examples of CSP | |
| 38 | Constraint Satisfaction Problems: Backtracking Search | |
| 39 | Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search | |
| 40 | Constraint Satisfaction Problems: Inference for detecting failures early | |
| 41 | Constraint Satisfaction Problems: Exploiting problem structure | |
| 42 | Logic in AI : Different Knowledge Representation systems - Part 1 | |
| 43 | Logic in AI : Syntax - Part - 2 | |
| 44 | Logic in AI : Semantics - Part - 3 | |
| 45 | Logic in AI : Forward Chaining - Part 4 | |
| 46 | Logic in AI : Resolution - Part - 5 | |
| 47 | Logic in AI : Reduction to Satisfiability Problems - Part - 6 | |
| 48 | Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7 | |
| 49 | Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8 | |
| 50 | Uncertainty in AI: Motivation | |
| 51 | Uncertainty in AI: Basics of Probability | |
| 52 | Uncertainty in AI: Conditional Independence & Bayes Rule | |
| 53 | Bayesian Networks: Syntax | |
| 54 | Bayesian Networks: Factoriziation | |
| 55 | Bayesian Networks: Conditional Independences and d-Separation | |
| 56 | Bayesian Networks: Inference using Variable Elimination | |
| 57 | Bayesian Networks: Reducing 3-SAT to Bayes Net | |
| 58 | Bayesian Networks: Rejection Sampling | |
| 59 | Bayesian Networks: Likelihood Weighting | |
| 60 | Bayesian Networks: MCMC with Gibbs Sampling | |
| 61 | Bayesian Networks: Maximum Likelihood Learning" | |
| 62 | Bayesian Networks: Maximum a-Posteriori LearningÃÂ | |
| 63 | Bayesian Networks: Bayesian Learning | |
| 64 | Bayesian Networks: Structure Learning and Expectation Maximization | |
| 65 | Introduction, Part 10: Agents and Environments | |
| 66 | Decision Theory: Steps in Decision Theory | |
| 67 | Decision Theory: Non Deterministic Uncertainty | |
| 68 | Probabilistic Uncertainty & Value of perfect information | |
| 69 | Expected Utility vs Expected Value | |
| 70 | Markov Decision Processes: Definition | |
| 71 | Markov Decision Processes: An example of a Policy | |
| 72 | Markov Decision Processes: Policy Evaluation using system of linear equations | |
| 73 | Markov Decision Processes: Iterative Policy Evaluation | |
| 74 | Markov Decision Processes: Value Iteration | |
| 75 | Markov Decision Processes: Policy Iteration and Applications & Extensions of MDPs | |
| 76 | Reinforcement Learning: Background | |
| 77 | Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning) | |
| 78 | Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning) | |
| 79 | Reinforcement Learning: TD Learning | |
| 80 | Reinforcement Learning: TD Learning and Computational Neuroscience | |
| 81 | Reinforcement Learning: Q Learning | |
| 82 | Reinforcement Learning: Exploration vs Exploitation Tradeoff | |
| 83 | Reinforcement Learning: Generalization in RL | |
| 84 | Deep Learning : Perceptrons and Activation functions | |
| 85 | Deep Learning : Example of Handwritten digit recognition | |
| 86 | Deep Learning : Neural Layer as matrix operations | |
| 87 | Deep Learning : Differentiable loss function | |
| 88 | Deep Learning : Backpropagation through a computational graph | |
| 89 | Deep Learning : Thin Deep Vs Fat Shallow Networks | |
| 90 | Deep Learning : Convolutional Neural Networks | |
| 91 | Deep Learning : Deep Reinforcement Learning | |
| 92 | Ethics of AI : Humans vs Robots | |
| 93 | Ethics of AI : Robustness and Transparency of AI systems | |
| 94 | Ethics of AI : Data Bias and Fairness of AI systems | |
| 95 | Ethics of AI : Accountability, privacy and Human-AI interaction | |
| 96 | Wrapup |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |
Machine Learning And Deep Learning - Fundamentals And Applications
| Course Status : | Completed |
| Course Type : | Core |
| Duration : | 12 weeks |
| Category : | |
| Credit Points : | 3 | Undergraduate/Postgraduate |
| Start Date : | 24 Jul 2023 |
| End Date : | 13 Oct 2023 |
| Enrollment Ends : | 07 Aug 2023 |
| Exam Registration Ends : | 18 Aug 2023 |
| Exam Date : | 29 Oct 2023 IST |
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An Introduction to Artificial Intelligence Week 3 Assignment Answer || Nptel Assignment week 3 #mrethic #nptel #nptel2023 #nptelsolution #artificialintellig...
Embark on a captivating journey into the realm of Artificial Intelligence (AI) and search methods for problem solving with our comprehensive guide to NPTEL's...
An Introduction to Artificial Intelligence. The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem.
An Introduction To Artificial Intelligence || NPTEL week 1 assignment answers|| #nptel #skumaredu
Nptel Assignment Answers 2024. Sorted: Introduction To Industry 4.0 And Industrial Internet Of Things Programming Data Structure And Algorithms Using Python Artificial Intelligence Search Methods For Problem Solving Machine Learning and Deep Learning - Fundamentals and Applications.
noc20 cs42 assigment 2. Swayay) NPTEL » An to Artificial Intel"gence Announcements About the Course Ask a Question Progress Mentor Unit 3 - Week 1 Course outline How does an NPTEL online course work? Assignment Zero Assignment 1 The due date for submitting this assignment has passed. As per our records you have not submitted this assignment.
Answer:-. Q10. Choose All correct statements about knowledge-based strong AI systems. Answer:-. For other courses answers:- Visit. For Internship and job updates:- Visit. Disclaimer: We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students, so we urge ...
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An Introduction to Artificial Intelligence Assignment 3 Answers:-. Q1. Consider the undirected graph below. Cost for each edge is written adjacent to the edge. S is the start node and G is the goal node. The TREE-SEARCH version of A* SEARCH is performed on this undirected graph. Assume that the heuristic function h for a node is the least ...
An Introduction to Artificial Intelligence by IIT Delhi course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem.
Re-evaluation for the course "An Introduction to Artificial Intelligence" Dear Student; Re-evaluation has been done by changing the answer for Question 7 in Assignment 10. Students are requested to find their revised scores of Assignment 10 in the Progress page. ... Here is the much-awaited announcement on registering for the Jan 2023 NPTEL ...
Fundamentals of Artificial Intelligence Quiz 3 Formalization of knowledge in a declarative form begins with a ___. A. conceptualization B. semantics C. interpretation D. model Formalization of knowledge in a declarative form begins with a conceptualization. Click to see answer P, Q, and R are logical propositions.
b. Knowledge about some specific domain is provided to such systems. c, The goal of such systems is to achieve human-level performance on one specific task. d. A* algorithm is an example of a strong AI system. Answer:-. An Introduction to Artificial Intelligence. Answers. Assignment 1.
The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as a...
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Answer:- (C) A, B, D, F, C, E, G. FOR NEXT WEEK ASSIGNMENT ANSWERS. NPTEL An Introduction to Artificial Intelligence Assignment 3 Answers 2022:- All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge. Disclaimer:- We do not claim 100% surety of solutions, these solutions are ...
This video is for providing on An Introduction To Artificial IntelligenceThis video is for Education PurposeThis Course is provided by NPTEL - Online course...
NPTEL :: Computer Science and Engineering - NOC:An Introduction to Artificial Intelligence. Courses. Computer Science and Engineering. NOC:An Introduction to Artificial Intelligence (Video) Syllabus. Co-ordinated by : IIT Delhi. Available from : 2019-11-13. Lec : 1.
An Introduction to Artificial Intelligence. The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem.
The agent should be able to imagine the consequence of its decisions to be able to identify the ones that work. In this first course on AI we study a wide variety of search methods that agents can employ for problem solving. In a follow up course - AI: Knowledge Representation and Reasoning - we will go into the details of how an agent can ...
#nptel #artificialintelligence #assignmentanswers Fundamentals Of Artificial IntelligenceIn this video, we're going to unlock the answers to the Fundamentals...
INTENDED AUDIENCE: UG, PG and PhD students and industry professionals who want to work in Machine and Deep Learning. PREREQUISITES: Knowledge of Linear Algebra, Probability and Random Process, PDE will be helpful. INDUSTRY SUPPORT: This is a very important course for industry professionals. Summary. Course Status : Completed. Course Type : Core.
The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as a...