Course: BIOSTAT 216: Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (Winter 2022)

Section outline

Select section Wednesday, January 5, 2022; 8:45 AM - 10:15 AM

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Wednesday, January 5, 2022; 8:45 AM - 10:15 AM
Lecture: What is Machine Learning? Introductory concepts

Supervised vs. Unsupervised learning. Overview of prediction techniques for supervised learning. Discussion of overfitting, loss functions, parametric vs. non-parametric models, and the bias-variance trade-off.

Location: MH-1400

Readings:

ISLR: Sec 2-2.2.2, Sec 3-3.2

ESL: Sec 1

References for R:

https://class.lambdamd.org/pdsr/

References for basic linear algebra:
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Select section Wednesday, January 12, 2022; 8:45 AM - 10:15 AM

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Wednesday, January 12, 2022; 8:45 AM - 10:15 AM
Lecture: Model fitting by optimization & Classification
Linear regression as an optimization procedure; Introduction to classification models: logistic regression, multinomial regression, linear discriminant analysis, K-nearest neighbors, receiver operating characteristic (ROC)
Readings:
ISLR: Sec 4-4.4.3
ESL: Sec 4 - 4.4
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Wednesday, January 12, 2022; 10:20 AM - 11:20 AM
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Select section Wednesday, January 19, 2022; 8:45 AM - 10:15 AM

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Wednesday, January 19, 2022; 8:45 AM - 10:15 AM
Lecture: Penalized Regression and Classification
Model selection by cross-validation; Low versus high-dimensional data; Regularization methods: Ridge, variable subset selection, Lasso

ISLR: Sections 6.1-6.2, 6.4
ESL: Sections 3.3-3.4
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Select section Wednesday, January 19, 2022; 10:20 AM - 11:20 AM

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Wednesday, January 19, 2022; 10:20 AM - 11:20 AM
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Select section Wednesday, January 26, 2022; 8:45 AM - 10:15 AM

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Wednesday, January 26, 2022; 8:45 AM - 10:15 AM
Lecture: Support Vector Machines
Maximum margin classifiers, support vector classifiers, support vector machines
ISLR: Sec 9
ESL: Sec 4.5, 12.1-12.3
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Select section Wednesday, February 2, 2022; 8:45 AM - 10:15 AM

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Wednesday, February 2, 2022; 8:45 AM - 10:15 AM
Lecture: Tree-based methods
Classification and regression trees; Bagging; Random forests
ISLR: Sec 8
ESL: Sec 9.2, 15
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Select section Wednesday, February 2, 2022; 10:20 AM - 11:20 AM

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Select section Wednesday, February 9, 2022; 8:45 AM - 10:15 AM

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Wednesday, February 9, 2022; 8:45 AM - 10:15 AM
Lecture: Boosting and gradient boosting
Boosting, Adaboost, Gradient boosted trees
ISLR: Sec 8.2.3
ESL: Sec 10
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Select section Wednesday, February 16, 2022; 8:45 AM - 10:15 AM

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Wednesday, February 16, 2022; 8:45 AM - 10:15 AM
Lecture: Unsupervised learning
Unsupervised learning: clustering and dimension reduction
ISLR: Sec 10
ESL: Sec 14.3, 14.5.1
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Select section Wednesday, February 16, 2022; 10:20 AM - 11:20 AM

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Select section Wednesday, February 23, 2022; 8:45 AM - 10:15 AM

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Wednesday, February 23, 2022; 8:45 AM - 10:15 AM
Lecture: Neural networks
Deep learning, Dense neural networks, Convolutional neural networks
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Select section Wednesday, March 2, 2022; 8:45 AM - 10:15 AM

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Wednesday, March 2, 2022; 8:45 AM - 10:15 AM
Lecture: Applications lecture: clustering and classification of high-dimensional data in a genomic context
Clustering and classification of high-dimensional data in a genomic context

Guest lecture by Adam Olshen
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Select section Wednesday, March 2, 2022; 10:20 AM - 11:20 AM

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Wednesday, March 2, 2022; 10:20 AM - 11:20 AM
Lab: We will discuss how to critique papers analyzing biomedical data using ML.
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Select section Wednesday, March 9, 2022; 8:45 AM - 10:15 AM

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Lecture 10: Updating clinical prediction models
Readings: Steyerberg "Clinical Prediction Models" Chapter 20
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		Home Overall Schedule Courses Programs Rosters
Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction BIOSTAT 216 (3 unit) . Course Director: Jean Feng, MS, PhD. Professor, Department of Epidemiology and Biostatistics

July 2026

Section outline

Lecture: Neural networks

Deep learning, Dense neural networks, Convolutional neural networks

Lab

Lecture 10: Updating clinical prediction models