ML_assignment_6

Axiom Bayesian Networks Biconditional Conjunction Decoding Disjunction Entailment Evaluating Filtering HMM (Hidden Markov Model) Implication Inference Knowledge Base Learning Negation Predicting Semantics Smoothing Stationary Process Syntax

Regression Naive Bayes Averaged One-Dependence Estimators (AODE) Bayesian Belief Network (BBN) Deep Boltzmann Machine (DBM) Bayesian Gaussian Nalve Bayes Deep Belief Networks (DBN) Deep Learning Multinomial Naive Bayes Bayesian Network (BN) Classification and Regression Tree (CART) Convolutional Neural Network (CNN) Stacked Auto-Encoders Random Forest Iterative Dichotomiser 3 (ID3) Gradient Boosting Machines (GBM) Boosting Bootstrapped Aggregation (Bagging) C4.5 C5.0 Ensemble Decision Tree Chi-squared Automatic Interaction Detection (CHAID) AdaBoost Stacked Generalization (Blending) Decision Stump Conditional Decision Trees Gradient Boosted Regression Trees (GBRT) MS Radial Basis Function Network (RBFN) Principal Component Analysis (PCA) Perceptron Neural Networks Partial Least Squares Regression (PLSR Back-Propagation Sammon Mapping Machine Learning Algorithms Hopfield Network Ridge Regression Least Absolute Shrinkage and Selection Operator (LASSO) Multidimensional Scaling (MDS)…

Generative Adversarial Networks (GANS) can be broken down into parts A-1 В-2 С-3 D-4 is used To learn a generative model, which describes how data is generated in terms of a probabilistic model A-Adversarial B-Generative C-Networks D-discriminator Which is one of the most popular also the most successful implementation of GAN? A-Conditional GAN B-Vanilla GAN C-Deep Convolutional GAN D-Laplacian Pyramid GAN Q2 : cite some application examples of GANS?

A Probabilistic Model for Approaching the Selection Pressure Curve

3. Consider a fuzzy inference system cosisting two fuzzy laws as follow, if u is A, and vis B₁, thenw is C₁ if u is A, and vis B₂, thenw is C₂ All the fuzzy sets are in the form of triangular (3-value) and trapizoidal (4-value) that are defined as follow, A₁ = (0,1,2, 4); B₁ = (1,3,5); C₁ = (1,2,3) A₂ = (1,3,4,6); B₂ = (0,2,4); C₂ = (2,4,6) Obtain the results of the fuzzy inference, (w), for the inputs u = 3,v₁ =1.5 using the Mamdani inference method in a precise and digramatic form.

For learning a predictive model describe the coefficient of determination and explicate its usefulness in Linear Regression

In the theoretical landscape of probability theory, a multifaceted inquiry unfolds: Can we dissect the complexities of stochastic processes and their role in modeling dynamic systems over time? How do concepts like Markov chains and Brownian motion contribute to understanding the evolution of random phenomena, transcending mere chance into a nuanced exploration of theoretical probabilities with real-world implications? Furthermore, how does the convergence of these theoretical frameworks amplify our ability to analyze and predict the uncertain trajectories of diverse phenomena in fields ranging from finance to physics?

Subject : Artificial Intelligence What Is Fuzzy Inference Systems? Select one: a. Having a larger output than the input b.  Having a smaller output than the input c. The process of formulating the mapping from a given input to an output using fuzzy logic d. Changing the output value to match the input value to give it an equal balance

Select what you think is correct (multiple options are possible)?   A) Logistic regression is a parametric classification algorithm and decision tree   B) Logistic regression is based on a linear combination of parameters as is decision tree   C) Logistic regression is based on a linear combination of parameters and a link function called sigmoid   D) Decision trees tend to have high bias and low variance that random forests fix   E) Decision trees use unsupervised learning

4 Bayesian Learning In Naive Bayes Classifier, what happens when the conditional independence Question 7 assumption is violated? Explain in terms of the consistency of the outcome (i.e., is the output the same as the target value).

machine learning models

Could you perhaps supply some examples to support your choice of machine learning model?

T or F

Is Logistic regression based on a linear combination of parameters as is decision tree?

Logistic regression aims to train the parameters from the training set D = {(x(i),y(i)), i 1,2,...,m, y ¤ {0,1}} so that the hypothesis function h(x) = g(0¹ x) 1 (here g(z) is the logistic or sigmod function g(z) can predict the probability of a 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0j = 0j + a(y(¹) — hz(x)))x; ave. =

Fuzzy Logic :

Finding a hypothesis that matches the examples is the first step in inductive learning.According to Ockham's razor, the simplest consistent theory should be chosen. The difficulty of this job is determined on the representation used. (True/False)

Classic statistical inference is based on the following three fundamental assumptions.What are they?

Which of the following statements are true for statistical learning?(select all that apply)   a) For big data, likelihood overwhelms prior, hence MAP converges to MLE.   b) MLE doesn't need prior distribution.   c) MAP learning needs prior distribution.   d)Full Bayesian learning gives best possible predictions but is intractable.

Please dont us Ai

Artificial Intelligence please answer

Incorrect Question 11 0 / 6 pts Which of the following supervised learning methods CANNOT use categorical input variables (i.e. categorical features, predictors)? O multinomial logistic regression naive Bayes classifier neural net random forest all the listed methods can use categorical features Olinear regression

Inductive learning involves finding a hypothesis that agrees well with the examples.Ockham’s razor suggests choosing the simplest consistent hypothesis. The difficultyof this task depends on the chosen representation.(True/False)

[9_3_B_BCD] Please answer this question step by step

True or False: During Q-learning, the learning rate α should be decreased as the Q-table is updated. Briefly justify your answer.

In non-monotonic thinking, there is uncertainty and incompleteness.

What are the differences between deep probabilistic models and traditional probabilistic models? Can you please share the pros and cons?

Question 10 Suppose we are using a Perceptron algorithm to predict if a point lies above or below the line y=2x-3. The next point in the test set is (1,2). The algorithm predicts that the point lies below the line. Question 9 Assuming the learning rate is 0.01 and the current value for the weights are: weight for x: 0.5 weight for y: 0.025 Suppose we are using a Perceptron algorithm to predict if a point lies above or below the line y=2x-3. The first point in the test set is (0,-2). The algorithm What is the new weight for x? predicts that the point lies below the line. O 5.02 What happens next? O 4.98 O The weights are NOT changed because the algorithm predicted correctly. O 5.2 O The weights are changed because the algorithm predicted incorrectly. O 4.8 Question 11 Question 12 Consider the logistic function Assume we are using Logistic Regression to predict the binary classification problem of whether a student should be admitted into a college based solely 1+e et upon their SAT…