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Fundamentals of Machine Learning

(CTU-AI210.AP1)
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1

Foundations of Machine Learning

  • Welcome
  • Scope, Terminology, Prediction, and Data
  • Putting the Machine in Machine Learning
  • Examples of Learning Systems
  • Evaluating Learning Systems
  • A Process for Building Learning Systems
  • Assumptions and Reality of Learning
  • About Our Setup
  • The Need for Mathematical Language
  • Our Software for Tackling Machine Learning
  • Probability
  • Linear Combinations, Weighted Sums, and Dot Products
  • A Geometric View: Points in Space
  • Notation and the Plus-One Trick
  • Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
  • NumPy versus “All the Maths”
  • Floating-Point Issues
2

Comparing Machine Learning Algorithms

  • Classification Tasks
  • A Simple Classification Dataset
  • Training and Testing: Don’t Teach to the Test
  • Evaluation: Grading the Exam
  • Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
  • Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
  • Simplistic Evaluation of Classifiers
  • A Simple Regression Dataset
  • Nearest-Neighbors Regression and Summary Statistics
  • Linear Regression and Errors
  • Optimization: Picking the Best Answer
  • Simple Evaluation and Comparison of Regressors
  • Revisiting Classification
  • Decision Trees
  • Support Vector Classifiers
  • Logistic Regression
  • Discriminant Analysis
  • Assumptions, Biases, and Classifiers
  • Comparison of Classifiers: Take Three
  • Linear Regression in the Penalty Box: Regularization
  • Support Vector Regression
  • Piecewise Constant Regression
  • Regression Trees
  • Comparison of Regressors: Take Three
  • Ensembles
  • Voting Ensembles
  • Bagging and Random Forests
  • Boosting
  • Comparing the Tree-Ensemble Methods
3

Building Machine Learning Models

  • Feature Engineering Terminology and Motivation
  • Feature Selection and Data Reduction: Taking out the Trash
  • Feature Scaling
  • Discretization
  • Categorical Coding
  • Relationships and Interactions
  • Target Manipulations
  • Models, Parameters, Hyperparameters
  • Tuning Hyperparameters
  • Down the Recursive Rabbit Hole: Nested Cross-Validation
  • Pipelines
  • Pipelines and Tuning Together
  • Feature Selection
  • Feature Construction with Kernels
  • Principal Components Analysis: An Unsupervised Technique
4

Evaluating Model Performance

  • Evaluation and Why Less Is More
  • Terminology for Learning Phases
  • Major Tom, There’s Something Wrong: Overfitting and Underfitting
  • From Errors to Costs
  • (Re)Sampling: Making More from Less
  • Break-It-Down: Deconstructing Error into Bias and Variance
  • Graphical Evaluation and Comparison
  • Comparing Learners with Cross-Validation
  • Baseline Classifiers
  • Beyond Accuracy: Metrics for Classification
  • ROC Curves
  • Another Take on Multiclass: One-versus-One
  • Precision-Recall Curves
  • Cumulative Response and Lift Curves
  • More Sophisticated Evaluation of Classifiers: Take Two
  • Baseline Regressors
  • Additional Measures for Regression
  • Residual Plots
  • A First Look at Standardization
  • Evaluating Regressors in a More Sophisticated Way: Take Two
5

Integrated Applications and Capstone

  • Working with Text
  • Clustering
  • Working with Images
  • Optimization
  • Linear Regression from Raw Materials
  • Building Logistic Regression from Raw Materials
  • SVM from Raw Materials
  • Neural Networks
  • Probabilistic Graphical Models

1

Foundations of Machine Learning

  • Plotting a Probability Distribution Graph
  • Using the zip Function
  • Calculating the Sum of Squares
  • Plotting a Line Graph
  • Plotting a 3D Graph
  • Plotting a Polynomial Graph
  • Using the numpy.dot() Method
2

Comparing Machine Learning Algorithms

  • Displaying Histograms
  • Defining an Outlier
  • Calculating the Median Value
  • Estimating the Multiple Regression Equation
  • Evaluating a Logistic Model
  • Creating a Covariance Matrix
  • Using the load_digits() Function
  • Illustrating a Less Consistent Relationship
  • Illustrating a Piecewise Constant Regression
  • Calculating the Mean Value
3

Building Machine Learning Models

  • Manipulating the Target
  • Manipulating the Input Space
  • Displaying a Correlation Matrix
  • Creating a Nonlinear Model
  • Performing a Principal Component Analysis
  • Using the Manifold Method
4

Evaluating Model Performance

  • Constructing a Swarm Plot
  • Using the describe() Method
  • Viewing Variance
  • Creating a Confusion Matrix
  • Creating an ROC Curve
  • Recreating an ROC Curve
  • Creating a Trendline Graph
  • Viewing the Standard Deviation
  • Constructing a Scatterplot
  • Evaluating the Prediction Error Rates
5

Integrated Applications and Capstone

  • Encoding Text
  • Building an Estimated Simple Linear Regression Equation

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