Course Learning Objectives

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.

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