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Course Learning Objectives

By the end of this course, you will be able to:
  • Describe supervised learning and its suitability for various tasks.
  • Explain key machine learning concepts such as classification, regression, overfitting, and the trade-off in model complexity.
  • Identify appropriate data preprocessing techniques for specific scenarios, provide reasons for their selection, and integrate them into machine learning pipelines.
  • Develop an intuitive understanding of common machine learning algorithms.
  • Build end-to-end supervised machine learning pipelines using Python and scikit-learn on real-world datasets.

Lecture Learning Objectives

Below are specific lecture learning objectives.

Introduction and Course Information

  • be able to explain the motivation to study machine learning;
  • be able to differentiate between supervised and unsupervised learning;
  • know how to navigate through the course material.

Lecture 1: Terminology, Baselines, Decision Trees

  • differentiate between classification and regression problems;
  • explain machine learning terminology such as features, targets, predictions, training, and error;
  • use DummyClassifier and DummyRegressor as baselines for machine learning problems;
  • explain the fit and predict paradigm and use score method of ML models;
  • broadly describe how decision tree prediction works;
  • use DecisionTreeClassifier and DecisionTreeRegressor to build decision trees using scikit-learn;
  • explain the difference between parameters and hyperparameters;
  • explain the concept of decision boundaries.

Lecture 2: Machine Learning Fundamentals

  • explain how decision boundaries change with the max_depth hyperparameter;
  • explain the concept of generalization;
  • split a dataset into train and test sets using train_test_split function;
  • explain the difference between train, validation, test, and "deployment" data;
  • identify the difference between training error, validation error, and test error;
  • explain cross-validation and use cross_val_score and cross_validate to calculate cross-validation error;
  • explain overfitting, underfitting, and the fundamental tradeoff;
  • state the golden rule.

Lecture 3: $k$-nearest neighbours ($k$-NNs), support vector machines (SVMs) with RBF kernel

  • explain the notion of similarity-based algorithms;
  • broadly describe how $k$-NNs use distances;
  • discuss the effect of using a small/large value of the hyperparameter $k$ when using the $k$-NN algorithm;
  • describe the problem of curse of dimensionality;
  • explain the general idea of SVMs with RBF kernel;
  • explain the differences between $k$-NNs and SVM RBFs;
  • broadly describe the relation of gamma and C hyperparameters with the fundamental tradeoff.

Lecture 4: Preprocessing and pipelines

  • identify when to implement feature transformations such as imputation, scaling, and one-hot encoding in a machine learning model development pipeline;
  • use sklearn for applying feature transformations on your dataset;
  • discuss golden rule in the context of feature transformations;
  • use sklearn.pipeline.Pipeline to build a preliminary machine learning pipeline;
  • use ColumnTransformer to build all our transformations together into one object and use it with sklearn pipelines.

Lecture 5: More on categorical features and encoding text data

  • explain handle_unknown="ignore" hyperparameter of scikit-learn's OneHotEncoder;
  • identify when it's appropriate to apply ordinal encoding vs one-hot encoding;
  • explain strategies to deal with categorical variables with too many categories;
  • explain why text data needs a different treatment than categorical variables;
  • use scikit-learn's CountVectorizer to encode text data;
  • explain different hyperparameters of CountVectorizer.

Lecture 6: Hyperparameter optimization and optimization bias

  • explain the need for hyperparameter optimization
  • carry out hyperparameter optimization using sklearn's GridSearchCV and RandomizedSearchCV
  • explain optimization bias
  • identify and reason when to trust and not trust reported accuracies

Lecture 7: Naive Bayes

  • Explain the naive assumption of naive Bayes.
  • Predict targets by hand on toy examples using naive Bayes.
  • Use scikit-learn's MultiNomialNB, BernoulliNB, and GaussianNB.
  • Use predict_proba for different classifiers and explain its usefulness.
  • Explain the need of smoothing in naive Bayes.
  • Explain how alpha controls the fundamental tradeoff.
  • Use naive Bayes for multi-class classification.
  • Name advantages and disadvantages of naive Bayes.

Lecture 8: Linear models and multi-class, meta-strategies

  • Explain the general intuition behind linear models
  • Explain the predict paradigm of linear models
  • Use scikit-learn's LogisticRegression classifier
    • Use fit, predict, predict_proba
    • Use coef_ to interpret the model weights
  • Compare logistic regression with naive Bayes
  • Explain the advantages and limitations of linear classifiers
  • Carry out multi-class classification using OVR and OVO strategies.