What are the three key components that determine the type of machine learning? Task, Performance Measure, Experience Input, Process, Output Data, Model, Evaluation Algorithm, Data, Output

Task, Performance Measure, Experience

What does Chapter 1 of the document 'Handbook of AI and Big Data in Investments' suggest as a primary reason(s) for investment professionals to gain a better understanding of AI and big data? To reduce investment risks to zero To automate all investment decisions To leverage these technologies for improving stock portfolio performance To replace human analysts with AI

To leverage these technologies for improving stock portfolio performance

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Computer Science - Software Engineering

user_striner Created by 7 mon ago

Cards in this deck(23)

What are the three key components that determine the type of machine learning?

T/F: Bayes' Theorem is used only when variables are independent and does not apply to conditional probabilities.

T/F: The sum rule of probability states that the marginal probability of a variable X can be obtained by summing over the joint probabilities of X with another variable Y.

T/F: Posterior probabilities are calculated without considering prior probabilities or the likelihood of new evidence affecting the outcome.

T/F: The product rule of probability states that the joint probability of two events X and Y can be expressed as the product of the conditional probability of Y given X and the probability of X.

T/F: Bayes' theorem provides a way to update the probability estimate for an event based on new evidence, effectively converting the prior probability into a posterior probability by incorporating the likelihood of the new evidence.

T/F: Prior probability refers to the initial estimate of the likelihood of an event before new data is taken into account, while posterior probability is the revised estimate after considering new evidence.

What does Chapter 1 of the document 'Handbook of AI and Big Data in Investments' suggest as a primary reason(s) for investment professionals to gain a better understanding of AI and big data?

T/F: Linear regression can model interactions among targets to explore the combined effects on the feature.

T/F: The process of a linear regression model typically involves maximizing the sum of the squared differences between observed and predicted values.

T/F: For linear regression, the relationship between targets and features is assumed to be linear.

T/F: In the linear regression model, the coefficients represent the intercept and rate of change of the targets with respect to the features.

T/F: A classification tree predicts the response by assigning the mean to the most commonly occurring class of training observations in the region to which it belongs.

A classification tree would be used when you need to predict _____ of something, like if an email is spam or not.

A regression tree is used when you need a _____ prediction like housing price.

What are the two types of decision trees?

T/F: In regression trees, predictions for a given observation are made using the mean response of the training observations within the same region.

T/F: Decision trees can be applied to both regression and classification tasks where they segment the predictor space into simpler regions. Predictions for an observation are based on the mean or mode of the responses in the region where it belongs.

T/F: Decision trees typically use a method called recursive binary splitting to segment the predictor space into regions, which are often high dimensional rectangles, boxes and diagonal lines.

T/F: Active managers believe that they can predict alpha.

T/F: CAPM theorizes that alpha will eventually be 0.

Which of the following best describes the role of market makers in investment markets?

What forms of analysis are prohibited by weak, semi-strong, and strong markets?

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