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Lecture 1: Introduction to CPSC 330
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What is machine learning, types of machine learning, learning to navigate through the course materials, getting familiar with the course policies
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Lecture 2: Terminology, Baselines, Decision Trees
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Terminology and Decision Trees
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Lecture 3: ML fundamentals
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generalization, data splitting, overfitting, underfitting, the fundamental tradeoff, the golden rule
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Lecture 4: \(k\)-nearest neighbours and SVM RBFs
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introduction to KNNs, hyperparameter n_neighbours or \(k\), C and gamma hyperparameters of SVM RBF, decision boundaries with different values of hyperparameters.
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Lecture 5: Preprocessing and sklearn pipelines
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Pre-processing, Transformations, and pipelines.
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CPSC 330 Lecture 8: Hyperparameter Optimization
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Linear regression, logistic regression, prediction probabilities, sigmoid, interpretation of coefficients
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CPSC 330 Lecture 9: Classification Metrics
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confusion metrics, precision, recall, f1-score, PR curves, AP score, ROC curve, ROC AUC, class imbalance
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CPSC 330 Lecture 10: Regression Metrics
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Regression metrics
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CPSC 330 Lecture 11: Ensembles
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CPSC 330 Lecture 12: Feature importances
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CPSC 330 Lecture 13: Feature Engineering and Selection
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CPSC 330 Lecture 14: K-Means
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CPSC 330 Lecture 15: DBSCAN, Hierarchical Clustering
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CPSC 330 Lecture 16: Recommender Systems
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CPSC 330 Lecture 17: Natural Language Processing
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CPSC 330 Lecture 19: Introduction to deep learning and computer vision
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CPSC 330 Lecture 20: Time series
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CPSC 330 Lecture 21: Survival analysis
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CPSC 330 Lecture 22: Communication
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