SSQ / Coursera-UW-Machine-Learning-Classification

Notebook for quick search

Coursera-UW-Machine-Learning-Classification

Course can be found in Coursera

Quiz answers for quick search can be found in my blog SSQ

• Week 1:
  • Linear Classifiers & Logistic Regression
    • decision boundaries
    • linear classifiers
    • class probability
    • logistic regression
    • impact of coefficient values on logistic regression output
    • 1-hot encoding
    • multiclass classification using the 1-versus-all
    • Predicting sentiment from product reviews
• Week 2:
  • Learning Linear Classifiers
    • Maximum likelihood estimation
    • Gradient ascent algorithm for learning logistic regression classifier
    • Choosing step size for gradient ascent/descent
    • (VERY OPTIONAL LESSON) Deriving gradient of logistic regression
    • Implementing logistic regression from scratch
  • Overfitting & Regularization in Logistic Regression
    • Overfitting in classification
    • Overconfident predictions due to overfitting
    • L2 regularized logistic regression
    • Sparse logistic regression
    • Implementing Logistic Regression with L2 regularization
• Week 3:
  • Decision Trees
    • Predicting loan defaults with decision trees
    • Learning decision trees
      • Recursive greedy algorithm
      • Learning a decision stump
      • Selecting best feature to split on
      • When to stop recursing
    • Using the learned decision tree
      • Traverse a decision tree to make predictions: Majority class predictions; Probability predictions; Multiclass classification
    • Learning decision trees with continuous inputs
      • Threshold splits for continuous inputs
      • (OPTIONAL) Picking the best threshold to split on
    • Identifying safe loans with decision trees
    • Implementing binary decision trees from scratch
• Week 4
  • Overfitting in decision trees
    • Identify when overfitting in decision trees
    • Prevent overfitting with early stopping
      • Limit tree depth
      • Do not consider splits that do not reduce classification error
      • Do not split intermediate nodes with only few points
    • Prevent overfitting by pruning complex trees
      • Use a total cost formula that balances classification error and tree complexity
      • Use total cost to merge potentially complex trees into simpler ones
    • Decision Trees in Practice for preventing overfitting
  • Handling missing data
    • Describe common ways to handling missing data:
      1. Skip all rows with any missing values
      2. Skip features with many missing values
      3. Impute missing values using other data points
    • Modify learning algorithm (decision trees) to handle missing data:
      1. Missing values get added to one branch of split
      2. Use classification error to determine where missing values go
• Week 5
  • Boosting
    • Identify notion ensemble classifiers
    • Formalize ensembles as the weighted combination of simpler classifiers
    • Outline the boosting framework – sequentially learn classifiers on weighted data
    • Describe the AdaBoost algorithm
      • Learn each classifier on weighted data
      • Compute coefficient of classifier
      • Recompute data weights
      • Normalize weights
    • Implement AdaBoost to create an ensemble of decision stumps
    • Discuss convergence properties of AdaBoost & how to pick the maximum number of iterations T
    • Exploring Ensemble Methods with pre-implemented gradient boosted trees
    • Implement your own boosting module
• Week 6
  • Evaluating classifiers: Precision & Recall
    • Classification accuracy/error are not always right metrics
    • Precision captures fraction of positive predictions that are correct
    • Recall captures fraction of positive data correctly identified by the model
    • Trade-off precision & recall by setting probability thresholds
    • Plot precision-recall curves.
    • Compare models by computing precision at k
    • Exploring precision and recall
• Week 7
  • Scaling to Huge Datasets & Online Learning
    • Significantly speedup learning algorithm using stochastic gradient
    • Describe intuition behind why stochastic gradient works
    • Apply stochastic gradient in practice
    • Describe online learning problems
    • Relate stochastic gradient to online learning
    • Training Logistic Regression via Stochastic Gradient Ascent