Machine Learning

Machine Learning

Machine Learning

Lessons

  1. Introduction

    1. What is the history of machine learning?

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    2. What is the difference between machine learning solution and programmatic solution?

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    3. What is a formal definition of machine learning?

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    4. What are some domains and examples of machine learning?

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    5. How can we create a (machine) learner?

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  2. Different types of Machine Learning

    1. What are the broad types of machine learning?

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    2. What is UnSupervised / Supervised / SemiSupervised and Reinforcement Learning?

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    3. What is supervised learning?(In detail)

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    4. What are some examples of Classification and Regression problems?

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    5. What are Features, Some of the Sample training examples of feature and Can we draw some Schematic Diagrams (for Supervised learning)?

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    6. What is Classification Learning? and what are some of its tasks and performance metric?

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    7. How do we get data for the learning problems? How are representations of functions used in machine learning? What is the hypothesis space?

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  3. Hypothesis Space and Inductive Bias

    1. What is inductive learning?

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    2. What are features and feature vectors?

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    3. What is the start of Classification problem. What is Feature Space and Hypothesis space for Classification problems?

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    4. 5 types of representations of a function

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    5. Hypothesis space

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    6. Terminology (example, training data, instrance space, concept, target function)

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    7. What is the Size of the Hypothesis space (for n boolean features) and What is Hypothesis language?

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    8. What is inductive learning hypothesis?

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    9. What is Inductive learning and consistent hypothesis? Why is Inductive learning an ill posed problem?

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    10. What are various types of bias? (Occums Razor, MDL, MM) and what are the important issues in Machine Learning? What is Generalization? (Bias and Variance)

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  4. Evaluation and Cross-Validation

    1. What is experimental evaluation of learning algorithms?

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    2. How do we Evaluate predictions? and What is absolute error? (Evaluate predictions)

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    3. What is sum of squares error and number of misclassification? (Evaluate predictions)

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    4. What is confusion matrix?

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    5. What is accuracy, precision and recall? (evaluate predictions)

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    6. What is sample error and true error?

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    7. What are the sources of errors?

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    8. What are the difficulties in evaluating hypothesis with limited data and possible solutions?

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    9. How can we evaluate with limited training data?

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    10. What is K fold cross validation trade off in machine learning?

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  5. Tutorial I

    1. Introduction to Tutorial I

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    2. Types of learning : supervised vs unsupervised learning

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    3. Example of supervised vs unsupervised learning

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    4. Types of features : categorical vs continuous features

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    5. Types of supervised learning: regression vs classification

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    6. Bias vs Variance

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    7. Generalization performance of a learning algorithm

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  6. Linear Regression

    1. What is regression? (Linear functions and other functions) and What are various Types of regression models?

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    2. What is linear regression?

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    3. Looking at an example of a training set for regression

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    4. What is multiple linear regression?

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    5. What assumption are we making for errors?

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    6. The least square regression line

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    7. How do we learn the parameters (for single regression and for multiple linear regression)

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    8. What is the delta or lms method and how do we use gradient descent?

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    9. What is lms update or delta rule, batch descent and stochastic gradient descent?

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  7. Introduction to Decision Trees

    1. What is a decision tree?

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    2. How to draw a sample decision trees for discrete data?

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    3. How to draw a sample decision trees for continuous data?

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    4. Generate a decision tree from training examples

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    5. Decision tree for playing tennis

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    6. Introduction to ID3 (searching for a good tree )

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  8. Learning Decision Tree

    1. How do we select attributes for decision tree? (information gain, entropy)

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    2. Example of creating a decision tree (using ID3 algorithm)

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    3. What is GINI Index?

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    4. How do we split continuous attributes and what are the practical issues in classification

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    5. Practical issues in classification

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  9. Overfitting

    1. What is overfitting?

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    2. An example of underfitting and overfitting

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    3. Overfitting due to noise or insufficient examples

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    4. How to avoid overfitting?

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    5. What is MDL?

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    6. What are the conditions for pre pruning?

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    7. How do we use reduced error pruning for post pruning?

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    8. What are the triple tradeoffs in model selection and generalization?

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    9. What is regularization?

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  10. Python Exercise on Decision Tree and Linear Regression

    1. Python exercise on linear regression

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    2. Python exercise on logistic regression

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    3. Python exercise on decision tree regression

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  11. Tutorial II

    1. How to solve a sample problem in linear regression?

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    2. How to solve problems related to decision trees?

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    3. How to find the entropy of a set and use in decision trees?

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    4. What is information gain?

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  12. K-Nearest Neighbour

    1. What is instance based learning and K-Nearest Neighbour algorithm?

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    2. What is the standard distance function (euclidean distance) and the 3 issues related to it?

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    3. What are some examples of K-Nearest Neighbour and what is the impact of k?

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    4. How can we use weighted distance functions?

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    5. Why do we need to remove extra features?

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    6. What are the various approaches to giving weights?

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  13. Feature Selection

    1. Why do we need feature reduction?

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    2. What is the curse of dimensionality?

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    3. How can we do feature reduction? (selection and extraction)

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    4. How can we evaluate feature subset? (wrapper / supervised and filter / unsupervised)

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    5. How can we use the feature selection algorithm? (forward and backward selection algorithm)

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    6. What are univariate feature selection methods?

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    7. What are multivariate feature selection methods?

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  14. Feature Extraction

    1. What is feature extraction and what kind of features do we want?

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    2. What are principal components (PCs) and how do we choose features?

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    3. How do we choose the direction of the principal components (PCs) and how do we use PCA?

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    4. How do we choose a feature (axis) for classification and how is Linear discriminant Analysis useful?

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  15. Collaborative Filtering

    1. What is a recommender system?

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    2. How can we formally define recommendation problem?

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    3. What are the two types of recommendation systems? (content, collaborative filtering)

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    4. What are the two types of collaborative filtering? (used based nearest nbr, item based nearest nbr)

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    5. What are the two phases of algorithms for collaborative filtering? (nbr formation, recommendation)

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    6. What are the issues with user based KNN CF?

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    7. What is item based collaborative filtering?

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  16. Python Exercise on KNN and PCA

    1. What we will cover?

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    2. How do we use KNeighborsClassifier in python?

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    3. How do we use randomized PCA in Python?

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    4. How can we do Face recognition using PCA and KNN?

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  17. Tutorial III

    1. What is the curse of dimensionality?

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    2. What is feature selection?

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    3. What is feature reduction and PCA? (principal component analysis)

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    4. How do you calculate the eigen values and eigen vector of a matrix?

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    5. What is K-NN (K Nearest Neighbour) Classification?

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  18. Bayesian Learning

    1. How is probability used for modelling concepts?

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    2. What is Bayes theorem?

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    3. Can we look at an example of Bayes theorem?

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    4. How can Bayes theorem be applied to find the hypothesis in machine learning? (MAP hypothesis)

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    5. What is Bayes optimal classifier?

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    6. Gibbs sampling

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  19. Naive Bayes

    1. Naive bayes algorithm

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    2. Naive bayes algorithm for discrete x

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    3. What is smoothing and why is it required?

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    4. Can we look at an example of naive bayes algorithm for discrete x?

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    5. How do we use smoothing when estimating parameters?

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    6. What is the assumption that we made in naive bayes and what happens if it is invalid?

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    7. What is gaussian naive bayes? (for continuous X, but discrete Y)

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    8. What are bayesian networks?

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  20. Bayesian Network

    1. Why do we need bayes network?

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    2. Can we look at an example of bayes network?

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    3. What does a bayesian network represent?

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    4. What can we do with a baynesian network (Inference)?

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    5. Where can we apply bayesian network?

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    6. How do we define a bayesian network?

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    7. What is the graphical representation of naive bayes model?

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    8. What is the hidden markov model?

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    9. How is learning helped by bayesian belief networks?

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  21. Python Exercise on Naive Bayes

    1. How to use the naive bayes classifier?

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    2. What is naive bayes classifier?

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    3. How is naive bayes classifier relevant in the context of email spam classification?

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  22. Tutorial IV

    1. How do we estimate the probabilities using the frequency distribution of probability?

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    2. How do we use bayes rule?

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    3. What is MAP inference?

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    4. What is naive bayes assumption?

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    5. What is bayesian networks (the structures), inference and marginalization?

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  23. Logistic Regression

    1. What is Logistic Regression (for Classification problems) and sigmoid function?

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    2. What are some of the Interesting Propreties of Sigmoid function?

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    3. How can we use stochastic gradient descent with logistic regression?

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  24. Introduction Support Vector Machine

    1. Support vector machine

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    2. Functional margin

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    3. Functional margin of a set of point

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    4. Solving the optimization problem

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  25. SVM The Dual Formulation

    1. Lagrangian duality in brief

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    2. The KKT conditions

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    3. Implication of Lagrangian

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    4. The dual problem

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  26. SVM Maximum Margin with Noise

    1. Linear SVM formulation

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    2. Limitation of previous SVM formulation

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    3. What objective to be minimized?

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    4. Lagrangian

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    5. Dual formulation

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  27. Nonlinear SVM and Kernel Function

    1. Non-linear SVM, feature space and kernel function

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    2. Kernel trick

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    3. Commonly used kernel function

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    4. Performance

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  28. SVM Solution to the Dual Problem

    1. SMO algorithm (sequential optimization)

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    2. Cordinate ascent

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    3. SMO (for dual problem)

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  29. Python Exercise on SVM

    1. Support vector classification

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    2. Visualize the decision boundaries

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    3. Load data

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  30. Introduction to NN

    1. Neural network and neuron

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    2. Perceptron - basic unit in NN

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    3. Gradient decent

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    4. Stochastic gradient descent

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    5. Multi-layer networks - by stochastic many NN

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  31. Multilayer Neural Network

    1. Limitation of perceptrons

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    2. Multi-layer NN

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    3. Power/ Expressiveness of multilayer networks

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    4. Two-layer back-propagation neural network

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    5. Learning for BP nets

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    6. Derivation

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  32. Neural Network and Backpropagation Algorithm

    1. Single layer perceptron and boolean functions (OR, XOR)

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    2. Representation capability of NNs

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    3. Learning in multi layer N using back propagation

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    4. Derivation

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    5. Back propagation algorithm

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    6. Training practices: batch vs stochastic and learning in epoch

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    7. Overfitting in anns and local minima

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  33. Deep Neural Network

    1. Deep learning

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    2. Hierarchical representation & unsupervised pre-training

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    3. Architecture & Training

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    4. Pooling

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    5. CNN properties

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  34. Python Exercise on Neural Network

    1. How can we create a artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?

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    2. How do we load dependencies (to recognize handwritten digits)?

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    3. How do we load the data (to recognize handwritten digits)?

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    4. How do we make the model (to recognize handwritten digits)?

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    5. How do we train the model (to recognize handwritten digits)?

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    6. What is our takeaway from this exercise (to recognize handwritten digits)?

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  35. Tutorial VI

    1. What is a perceptron?

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    2. What is perceptron learning rule?

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    3. How do we represent a boolean function using a perceptron?

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    4. What is forward and backward pass algorithm or backpropagation algorithm?

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    5. Stochastic gradient descent and batch gradient descent

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    6. Quick overview of some deep learning algorithms

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  36. Introduction to Computational Learning Theory

    1. Goal of learning theory & Core aspect of machine learning

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    2. PAC

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    3. Prototypical concept learning task

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  37. Sample Complexity Finite Hypothesis Space

    1. What is Sample Complexity?

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    2. Can we look at an example of consistent case?

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    3. What is Find-S algorithm and what can it do?

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  38. VC Dimension

    1. What kind of theorems do we have when hypothesis state is infinite?

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    2. What is shattering?

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    3. What is the definition of VC dimension?

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    4. What is the upper bound and lower band on sample complexity with VC?

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  39. Introduction to Ensembles

    1. What is ensemble learning?

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    2. How can we use weak learners?

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    3. How can we combine learners in Bayesian classifiers?

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    4. Why are ensembles successful and what are the main challenges with them?

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  40. Bagging and Boosting

    1. What is Bagging?

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    2. What is Boosting and what is AdaBoost?

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    3. Why does ensembling work?

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  41. Introduction to Clustering

    1. What is unsupervised learning and clustering?

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    2. What are some applications of clustering, and what are various aspectis of clustering?

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    3. Major clustering approaches

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    4. How can we measure the quality of clustering?

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  42. Kmeans Clustering

    1. What is K-means algorithm?

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    2. How can we describe K-means Algorithm, and can we look at an illustration of it?

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    3. What are the similarity and distance measures?

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    4. What is the proof of convergence of K-means, time complexity, advantages and disadvantages?

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    5. What is model based clustering?

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    6. How can we apply K-means on a RGB image?

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    7. What is EM algorithm?

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  43. Agglomerative Hierarchical Clustering

    1. What is hierarchical clustering, bottom up and top down clustering?

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    2. What is a Dendrogram?

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    3. What is the algorithm for Agglomerative Hierarchical Clustering?

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    4. What is the complete link method?

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    5. What is average link clustering?

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  44. Python Exercise on kmeans clustering

    1. Can we look at python code for K means algorithm?

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    2. Can we look at python code for gaussian mixture model?

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    3. Hierarchical agglomerative clustering

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  45. Tutorial VIII

    1. What is K-means clustering?

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    2. Solving a sample problem n K-means clustering

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    3. What is agglomorative hierarchical clustering?

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    4. What is gaussian mixture model?

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