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If S is a linear vector space, what are the properties of S?
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If S is a linear vector space, what are the properties of S?
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What is the notation to represent a vector of real numbers or real-complex numbers?
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Upper case bold letter -- matrix or scalar or vector?
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Upper case non-bold letter -- matrix or scalar or vector?
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Is a vector usually a column vector? what is the ith column in a Matrix A? $a_i$ what does it mean? $a_j^T$ what does it mean? How is the $j^th$ element represented?
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What do you mean by matrix dimensions?
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Can you represent a matrix of real numbers with m * n dimensions with notations?
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Can you represent a matrix of complex numbers with m * n dimensions with notations?
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What is the outcome of matrix multiplications? is the multiplication outcome a number?
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What are tall matrices? What are short matrices?
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What is a square matrix?
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What is a diagonal matrix?
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What does dimension mean for a single vector?
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What are the various interpretations of matrix multiplication operations?
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What are matrix determinants?
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What is the significance of matrix determinants?
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What is a vector norm?
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What is linear dependence?
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What is a subspace?
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What are the related ideas of subspace?
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What is a row space?
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What is a column space?
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What is the rank of a matrix?
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What is the Big Block perspective of Matrix Multiplication?
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Do the multiplication approaches apply to the big block cases? and/or vice versa.
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What is linear independence?
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What are the different types of Matrix multiplication?
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Difference: Auto-Regressive and Auto Correlation.
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What is a lattice filter structure?
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Give some examples of Auto-Regressive Processes?
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What is the relation between Auto-Regressive Process and Toeplitz systems?
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Can we say that voice and video signals, sinewaves-plus-noise, signals in control systems and signals induced by earthquakes are some examples of Auto-Regressive Processes?
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What is Levinson Durbin Recursion?
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What is an AR system?
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Can you utilize the structure of a Toeplitz matrix to find solutions that have lower algorithmic (flops, operation count) complexity?
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What is adaptive filtering?
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What do the covariance matrices of Stationary signals look?
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Can you solve Toeplitz systems using Gaussian elimination?
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How does a Toeplitz Matrix relate to Stationary or non-stationary signals?
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What is Gaussian elimination?
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Can you solve Toeplitz systems using Cholesky Methods?
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If you solve Toeplitz systems using Gaussian Eliminations, what is the algorithmic complexity (algorithm)?
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Define Cholesky Methods?
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What is a Toeplitz Matrix?
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Will in a Toeplitz Matrix, all elements in a diagonal be the same? If so, how i.e. give examples?
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What is a Toeplitz Matrix?
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Will in a Toeplitz Matrix, the elements parallel to the diagonal be the same? If so, how?
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Do you mix test data with training data at all - at any time?
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What do we achieve with boosting Bagging and Stacking?
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In real-life: will you choose the best algorithm? or you can just use the most familiar algorithm to you. What are the pros and cons.
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What is MLOps?
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How do Tensorflow- PyTorch- MXNet differ from AWS SageMaker?
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What are some components and/or steps in AWS Sagemaker?
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Sagemaker build- Sagemaker Train- Sagemaker deploy: what are these?
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Do boosting Bagging and Stacking relate to only one algorithm such as Decsion tree? How or how not?
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What is Amazon Native AI/ML service called?
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Did you ever work with Sage Maker?
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Will you invest too much time to select the best algorithm?
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What are some practical tools used for machine learning and related?
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What are some core and common challenges in Machine Learning?
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Do you assume that corporate leaders always can ask interesting questions that ML can answer?
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What are the differences between k-nearest neighbor and k-means clustering?
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Are k-nearest neighbor and k-means clustering both supervised and/or unsupervised?
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How is the computational complexity of k-nearest neighbor and k-means clustering? Are they do once and use always solution?
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If the Data Changes i.e. new data comes what happens to k-nearest neighbor and k-means clustering?
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What is a decision tree?
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What is entropy in decision tree?
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What is the assumption of the Input in Naive Bayes?
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What is Regression? When do we use Regression? Is Regression a Learning? i.e. a Machine Learning?
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What is Bagging?
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What is Stacking?
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What is Naive Bayes? Why is it called Naive Bayes?
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Does Naive Bayes uses Conditonal Probability? If so how and why?
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Bias and Variance in the prediction and corresponding effect. Can you explain that?
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What do desire in bias and variance in the prediction. Answer in terms of High and Low.
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What are some core and/or popular Machine Learning Algorithms.
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What is boosting?
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Bias and Variance in the data and corresponding effect. Can you explain that?
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What is VC dimension?
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What is Semi-Supervised Machine Learning? Give some examples? When do we use semi-supervised Machine Learning? Best fit for what?
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What is Reinforcement Learning? Give some examples? When do we use Reinforcement Learning? Best fit for what?
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Can we do Reinforcement Learning without training step? If so how? Can you name any approach/algorithm if any in this regard?
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What do you mean by Exploration and Exploitation when it comes down to Reinforcement Learning?
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What are some important concepts to learn in Machine Learning?
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What is Machine Learning Anyway?
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What is the difference between Machine Learning and Artificial Intelligence?
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What are the different types of Machine Learning?
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What is Supervised Machine Learning? Give some examples? When do we use Supervised Machine Learning? Best fit for what?
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What is Unsupervised Machine Learning? Give some examples? When do we use Unsupervised Machine Learning? Best fit for what?
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Give examples?
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What is regularization?
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What is LMS algorithm? Linear Mean Square.
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What is the role of QR decomposition in LS analysis?
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What is QR decomposition?
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PLS and Machine Learning. How are they related?
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What is PLS? Partial Least Square?
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What is principle component analysis?
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What is Linear Least Square?
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What are Generalized Eigenvalues?
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What is an Ordinary LS?