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IIT GATE Exam 2024 : CS - Computer Science and Information Technology : Code

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MODULE 2: SOLVING PROBLEMS BYSEARCHING SUMMARY Informed search methods may have access to a heuristic function h(n) that estimates the cost of a solution from n. o The generic best-first search algorithm selects a node for expansion according to an evaluation function. o Greedy best-first search expands nodes with minimal h(n). It is not optimal but is often efficient. o A*search expands nodes with minimal f(n) = g(n) + h(n). A is complete and optimal, provided that h(n) is admissible (for TREE-SEARCH) or consistent (for GRAPHSEARCH). The space complexity of A* is still prohibitive. The performance of heuristic search algorithms depends on the quality of the heuristic function. One can sometimes construct good heuristics by relaxing the problem definition, by storing precomputed solution costs for subproblems in a pattern database, or by learning from experience with the problem class QUESTIONS Practice questions: 1. 2. 3. 4. 5. Explain Greedy best first search with respect to informed search strategies Explain A*search with respect to informed search strategies With respect to A* search discuss conditions for optimality What do you mean by optimality of A*? Discuss the heuristics for 8 puzzle example MODULE 2: Introduction to Machine learning, Understanding Data Introduction to Machine learning o o o o o o o Need for machine learning Machine learning explained Machine learning relation to other fields Types of machine learning Challenges of machine learning Machine learning process Machine learning applications Understanding Data o What is data? o Big data analytics and types of analytics o Big data analysis framework o Descriptive statistics o Univariate data analysis and visualization o Bivariate data and multivariate data o Multivariate statistics o Essential mathematics for multivariate data o Overview of hypothesis o Feature engineering and dimensionality reduction techniques SUMMARY 1. Machine learning can enable to extract the knowledge from the data stored in various archives to facilitate decision making 2. Machine learning is an important sub branch of AI 3. A model is an explicit description of patterns within the data 4. A model can be a formula, procedure or representation that can generate data decisions 5. Humans predict by remembering the past, then formulate the strategy and make a prediction. In the same manner, the computers can predict by following the process 6. The aim of AI is to develop intelligent systems. An agent can be a robot, humans or other autonomous systems 7. Deep learning is a branch of machine learning. The difference between machine learning and deep learning is that models are constructed using neural network technology in deep learning. Neural networks are models constructed based on the human neurons models 8. Data science deal with gathering of data for analysis 9. Data analysis aims to extract useful knowledge from crude data. Predictive analytics is an area dedicated for making decisions. Machine learning closely related to branch of analytics and shares almost all algorithms 10. Two types of data Label and unlablel data. Supervisor algorithm use labeled data set. Classification is a supervised learning method. The relationship between the input and target variables is represented in the form of structure which is called classification model 11. Cluster analysis is an example of unsupervised learning 12. Semi supervised algorithm assigns a pseudo label for unlabelled data 13. Reinforcement algorithm allows the agent to interact with the environment to get rewards. The agent can be a human, animal, robot, or any independent program. The reward enable the agent to gain experience 14. Data must be converted to information 15. Data analysis is an operation that converts data to information. Data analysis is a general area encompassing data analysis 16. Exploratory data analysis aims to understand data better 17. Descriptive statistics aims to summarize the data and data visualization aims to understand the data using charts 18. Data types Qualitative data types includes nominal and ordinal, quantitative data types include interval and ratio 19. Measure of central tendency include mean, median, mode and midrange 20. The most common dispersion measures are range, 5 number summary, interquartile range, and standard deviation 21. Skewness and kurtosis are shape measures. Skewness, kurtosis, mean, absolute deviation and coefficient of variation help in assessing the shape. 22. Visualization is an important aspect of data mining which helps the user to recognize and interpret the results quickly 23. Covariance is a measure of joint probability of random variables, and used to measure variance between two dimensions. Correlation measures the strength of linear relationship among variables 24. The idea of feature reduction is to transform a given set of measurements to a new set of features, so that the features exhibit a high information packing properties. This leads to a reduced and compact set of features 25. Sampling is a statistical procedure of data deduction statistics starts by identifying the groups for carrying out a study 26. Statistical methods are used to confirm or reject the hypothesis (Assumption of experiment is hypothesis), Z-test, t-tests, Chi square tests are used to evaluate QUESTIONS Practice Questions 1. 2. 3. 4. List the factors that drive the popularity of machine learning How is machine learning linked to AI, Data science and statistics List the types of machine learning Are classification and clustering are same or different? Justify 5. List out the difference between labeled and unlabelled data 6. What are the difference between classification and regression? 7. List the difference between reinforced learning and supervised learning 8. Explain the machine learning process model 9. Briefly explain classification algorithms 10. Explain the types of big data with an example 11. Explain in detail data cleaning processes 12. Explain univariate data analysis 13. Explain five charts in detail that helps data visualization 14. Explain the procedure of SVD 15. Explain the process of obtaining principle components and its relevance in feature reduction 16. Explain the procedure of hypothesis testing 17. Explain the procedure for pair t-tests and chi square goodness fit test Assignment Questions 18. Let us assume regression algorithms generates a model y=0.54+0.66 x for data pertaining to week sales data of a product. Here x, is the week and y is the product sales. Find the prediction for the 5th and 8th week 19. Give two examples of pattern and models 20. List 5 products that use machine learning 21. For a given univariate data set S={5, 10, 15, 20, 25, 30} of marks, find mean, median, mode, standard deviation and variance 22. For a given univariate data set S={5, 10, 15, 20, 25, 30} of marks, find arithmetic mean, geometric mean 23. For a given univariate data set S={5, 10, 15, 20, 25, 30} of marks, find five-point summary and plot box chart 24. Perform the descriptive analysis of data for the table 1 and 2 Age 1 weight 4.2 2 4.5 3 4.7 Table 1: Sample data 4 5 6 5.2 6 6.2 7 7 Table 2: student marks SID Eng Hindi Maths science 1 45 70.5 90 40 2 60 72.5 80 45 3 60 80 90 50 4 80 80 90 80 5 85 72 70 60 a) Find the min and max marks scored in each subject b) Find the details of student who scored highest marks in maths c) Find the student with marks English>60 and maths>70 8 7.2 9 7.5 10 8.5 25. For the univariate attribute such as weight, English and maths marks for the table 1 and 2, find the followings a) Mean, median, mode b) Weighted mean, geometric mean, harmonic mean c) Variance and standard deviation d) Absolute deviation, mean absolute deviation, median absolute deviation e) Coefficient of variation f) Skewness and kurtosis g) Five point summary, IQR, Semi quartile 26. For the bivariate data such as English and maths marks for the table 1 and 2, find the followings a) Covariance and correlation between two variables b) Covariance between English and Hindi Marks 27. Use appropriate data visualization to plot the table 1 using following charts a) Bar plot and pie chart b) Histogram, box plot and QQPLOT c) Dot plot, line chart and scatter plot d) Stem and leaf plot 28. Solve the following set of equations using Gauss elimination method 2x1+5 x2=7 6x1+12 x2=18 29. Solve the following set of equations using LU Decomposition method 2x1+5 x2=7 6x1+12 x2=18 4 30. Apply PCA for the following matrix [ 1 31. Apply SVD for the following matrix [ 3 ] 2 4 3 ] 1 2 32. Find covariance and correlation coefficients for the following two sets of data X: 1 2 6 12 Y: 8 12 18 22

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