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update the course information and schedule page
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czopluoglu committed Sep 19, 2023
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4 changes: 2 additions & 2 deletions README.md
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# EDLD 654: Applied Machine Learning for Educational Data Science

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This repo houses all the materials for a course on applied machine learning using [#rstats](https://twitter.com/hashtag/rstats) taught during the Fall 2022 term in the College of Education at the University of Oregon.
This repo houses all the materials for a course on applied machine learning using [#rstats](https://twitter.com/hashtag/rstats) taught during the Fall 2023 term in the College of Education at the University of Oregon.

For more information please contact Dr. Cengiz Zopluoglu ([email protected])
14 changes: 7 additions & 7 deletions _site/data/schedule.csv
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date,notes,notes_link,kaggle,kaggle_link,slides,slides_link,reading,reading_link,assignments,assignments_link
3-Oct,Lecture-1,https://edld654-fall22.netlify.app/lecture-1.html,Notebook-1,https://www.kaggle.com/code/uocoeeds/week-1-introduction/notebook,Week 1,https://edld654-fall22.netlify.app/slides/slide1.html,Kaggle CommonLit Readability Competition;NIJ Recidivism Challenge,https://www.kaggle.com/c/commonlitreadabilityprize/;https://nij.ojp.gov/funding/recidivism-forecasting-challenge,,
10-Oct,Lecture-2a;Lecture-2b,https://edld654-fall22.netlify.app/lecture-2a.html;https://edld654-fall22.netlify.app/lecture-2b.html,Notebook-2a;Notebook-2b; The {recipes} demo,https://www.kaggle.com/code/uocoeeds/lecture-2a-data-preprocessing-i;https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii;https://www.kaggle.com/code/uocoeeds/the-recipes-package-demo,Week 2,https://edld654-fall22.netlify.app/slides/slide2.html,"Kuhn & Johnson, Ch. 6;Kuhn & Johnson, Ch. 5;Boehmke & Greenwell, Ch. 3",http://www.feat.engineering/engineering-numeric-predictors.html;http://www.feat.engineering/encoding-categorical-predictors.html;https://bradleyboehmke.github.io/HOML/engineering.html,Assignment 1,https://www.kaggle.com/code/uocoeeds/assignment-1/
17-Oct & 24-Oct,Lecture-3a;Lecture-3b,https://edld654-fall22.netlify.app/lecture-3a.html;https://edld654-fall22.netlify.app/lecture-3b.html,Notebook-3a; Notebook-3b; Building a Linear Model with `caret`,https://www.kaggle.com/code/uocoeeds/lecture-3a-overview-of-linear-regression;https://www.kaggle.com/code/uocoeeds/lecture-3b-review-of-bias-variance-tradeoff;https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-with-cross-validation,Week 3 & 4,https://edld654-fall22.netlify.app/slides/slide3.html," Boehmke & Greenwell, Ch. 2;Boehmke & Greenwell, Ch. 4; James et al. Ch.3; Kuhn and Johnson, APM, Ch. 4, 5.1, and Ch.6",https://bradleyboehmke.github.io/HOML/process.html;https://bradleyboehmke.github.io/HOML/linear-regression.html;https://hastie.su.domains/ISLR2/ISLRv2_website.pdf;https://edld654-fall22.netlify.app/schedule.html,,
31-Oct,Lecture-4,https://edld654-fall22.netlify.app/lecture-4.html,Notebook-4; Building a Ridge Regression Model;Building a Lasso Regression Model; Building an Elastic Net Model; Using the Prediction Models for a New Text,https://www.kaggle.com/code/uocoeeds/lecture-4-regularized-penalized-regression;https://www.kaggle.com/code/uocoeeds/building-a-ridge-regression-model;https://www.kaggle.com/code/uocoeeds/building-a-lasso-regression-model;https://www.kaggle.com/code/uocoeeds/building-a-regression-model-with-elastic-net;https://www.kaggle.com/code/uocoeeds/using-the-prediction-models-for-a-new-text,Week 5,https://edld654-fall22.netlify.app/slides/slide4.html," Boehmke & Greenwell, Ch. 6; James et al., Ch.6.2",https://bradleyboehmke.github.io/HOML/regularized-regression.html; https://edld654-fall22.netlify.app/schedule.html,,
7-Nov & 14-Nov,Lecture-5,https://edld654-fall22.netlify.app/lecture-5.html,Building a Logistic Regression Model;Building a Classification Model with Ridge Penalty;Building a Classification Model with Lasso Penalty;Building a Classification Model with Elastic Net,https://www.kaggle.com/code/uocoeeds/building-a-logistic-regression-model/notebook;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-ridge-penalty/;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-lasso-penalty;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-elastic-net,Week 6 & 7,https://edld654-fall22.netlify.app/slides/slide5.html,"Boehmke & Greenwell, Ch. 5; James et al., Ch. 4.3; APM, Ch. 12.2 and 12.5",https://bradleyboehmke.github.io/HOML/logistic-regression.html;https://edld654-fall22.netlify.app/schedule.html;https://edld654-fall22.netlify.app/schedule.html,Assignment 2,https://www.kaggle.com/code/uocoeeds/assignment-2
21-Nov,Lecture-6a;Lecture-6b,https://edld654-fall22.netlify.app/lecture-6a.html;https://edld654-fall22.netlify.app/lecture-6b.html,Building a Prediction Model with K-nearest neighbors;Building a Classification Model with K-nearest neighbors;Building a Prediction Model with a Decision Tree;Building a Classification Model with a Decision Tree,https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-using-knn;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-using-knn;https://www.kaggle.com/uocoeeds/building-a-prediction-model-using-a-decision-tree;https://www.kaggle.com/code/uocoeeds/building-a-classification-tree-algorithm,Week 8_Part 1;Week 8_Part 2,https://edld654-fall22.netlify.app/slides/slide6a.html;https://edld654-fall22.netlify.app/slides/slide6b.html,"Boehmke & Greenwell, Ch. 8; Boehmke & Greenwell, Ch. 9; APM Ch. 13.5; James et al. Ch. 8.1",https://bradleyboehmke.github.io/HOML/knn.html;https://bradleyboehmke.github.io/HOML/DT.html;https://edld654-fall22.netlify.app/schedule.html;https://edld654-fall22.netlify.app/schedule.html,,
28-Nov,Lecture-7a;Lecture-7b,https://edld654-fall22.netlify.app/lecture-7a.html;https://edld654-fall22.netlify.app/lecture-7b.html,,,,,"Boehmke & Greenwell, Ch. 10; Boehmke & Greenwell, Ch. 11; Boehmke & Greenwell, Ch. 12; APM Ch. 8 & 14; James et al. Ch. 8.2",https://bradleyboehmke.github.io/HOML/bagging.html;https://bradleyboehmke.github.io/HOML/random-forest.html;https://bradleyboehmke.github.io/HOML/gbm.html; https://edld654-fall22.netlify.app/schedule.html; https://edld654-fall22.netlify.app/schedule.html,Assignment 3,https://www.kaggle.com/code/uocoeeds/assignment-3
3-Oct,Lecture-1,https://edld654-fall23.netlify.app/lecture-1.html,Notebook-1,https://www.kaggle.com/code/uocoeeds/week-1-introduction/notebook,Week 1,https://edld654-fall23.netlify.app/slides/slide1.html,Kaggle CommonLit Readability Competition;NIJ Recidivism Challenge,https://www.kaggle.com/c/commonlitreadabilityprize/;https://nij.ojp.gov/funding/recidivism-forecasting-challenge,,
10-Oct,Lecture-2a;Lecture-2b,https://edld654-fall23.netlify.app/lecture-2a.html;https://edld654-fall23.netlify.app/lecture-2b.html,Notebook-2a;Notebook-2b; The {recipes} demo,https://www.kaggle.com/code/uocoeeds/lecture-2a-data-preprocessing-i;https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii;https://www.kaggle.com/code/uocoeeds/the-recipes-package-demo,Week 2,https://edld654-fall23.netlify.app/slides/slide2.html,"Kuhn & Johnson, Ch. 6;Kuhn & Johnson, Ch. 5;Boehmke & Greenwell, Ch. 3",http://www.feat.engineering/engineering-numeric-predictors.html;http://www.feat.engineering/encoding-categorical-predictors.html;https://bradleyboehmke.github.io/HOML/engineering.html,Assignment 1,https://www.kaggle.com/code/uocoeeds/assignment-1/
17-Oct & 24-Oct,Lecture-3a;Lecture-3b,https://edld654-fall23.netlify.app/lecture-3a.html;https://edld654-fall23.netlify.app/lecture-3b.html,Notebook-3a; Notebook-3b; Building a Linear Model with `caret`,https://www.kaggle.com/code/uocoeeds/lecture-3a-overview-of-linear-regression;https://www.kaggle.com/code/uocoeeds/lecture-3b-review-of-bias-variance-tradeoff;https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-with-cross-validation,Week 3 & 4,https://edld654-fall23.netlify.app/slides/slide3.html," Boehmke & Greenwell, Ch. 2;Boehmke & Greenwell, Ch. 4; James et al. Ch.3; Kuhn and Johnson, APM, Ch. 4, 5.1, and Ch.6",https://bradleyboehmke.github.io/HOML/process.html;https://bradleyboehmke.github.io/HOML/linear-regression.html;https://hastie.su.domains/ISLR2/ISLRv2_website.pdf;https://edld654-fall23.netlify.app/schedule.html,,
31-Oct,Lecture-4,https://edld654-fall23.netlify.app/lecture-4.html,Notebook-4; Building a Ridge Regression Model;Building a Lasso Regression Model; Building an Elastic Net Model; Using the Prediction Models for a New Text,https://www.kaggle.com/code/uocoeeds/lecture-4-regularized-penalized-regression;https://www.kaggle.com/code/uocoeeds/building-a-ridge-regression-model;https://www.kaggle.com/code/uocoeeds/building-a-lasso-regression-model;https://www.kaggle.com/code/uocoeeds/building-a-regression-model-with-elastic-net;https://www.kaggle.com/code/uocoeeds/using-the-prediction-models-for-a-new-text,Week 5,https://edld654-fall23.netlify.app/slides/slide4.html," Boehmke & Greenwell, Ch. 6; James et al., Ch.6.2",https://bradleyboehmke.github.io/HOML/regularized-regression.html; https://edld654-fall23.netlify.app/schedule.html,,
7-Nov & 14-Nov,Lecture-5,https://edld654-fall23.netlify.app/lecture-5.html,Building a Logistic Regression Model;Building a Classification Model with Ridge Penalty;Building a Classification Model with Lasso Penalty;Building a Classification Model with Elastic Net,https://www.kaggle.com/code/uocoeeds/building-a-logistic-regression-model/notebook;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-ridge-penalty/;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-lasso-penalty;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-elastic-net,Week 6 & 7,https://edld654-fall23.netlify.app/slides/slide5.html,"Boehmke & Greenwell, Ch. 5; James et al., Ch. 4.3; APM, Ch. 12.2 and 12.5",https://bradleyboehmke.github.io/HOML/logistic-regression.html;https://edld654-fall23.netlify.app/schedule.html;https://edld654-fall23.netlify.app/schedule.html,Assignment 2,https://www.kaggle.com/code/uocoeeds/assignment-2
21-Nov,Lecture-6a;Lecture-6b,https://edld654-fall23.netlify.app/lecture-6a.html;https://edld654-fall23.netlify.app/lecture-6b.html,Building a Prediction Model with K-nearest neighbors;Building a Classification Model with K-nearest neighbors;Building a Prediction Model with a Decision Tree;Building a Classification Model with a Decision Tree,https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-using-knn;https://www.kaggle.com/code/uocoeeds/building-a-classification-model-using-knn;https://www.kaggle.com/uocoeeds/building-a-prediction-model-using-a-decision-tree;https://www.kaggle.com/code/uocoeeds/building-a-classification-tree-algorithm,Week 8_Part 1;Week 8_Part 2,https://edld654-fall23.netlify.app/slides/slide6a.html;https://edld654-fall23.netlify.app/slides/slide6b.html,"Boehmke & Greenwell, Ch. 8; Boehmke & Greenwell, Ch. 9; APM Ch. 13.5; James et al. Ch. 8.1",https://bradleyboehmke.github.io/HOML/knn.html;https://bradleyboehmke.github.io/HOML/DT.html;https://edld654-fall23.netlify.app/schedule.html;https://edld654-fall23.netlify.app/schedule.html,,
28-Nov,Lecture-7a;Lecture-7b,https://edld654-fall23.netlify.app/lecture-7a.html;https://edld654-fall23.netlify.app/lecture-7b.html,,,,,"Boehmke & Greenwell, Ch. 10; Boehmke & Greenwell, Ch. 11; Boehmke & Greenwell, Ch. 12; APM Ch. 8 & 14; James et al. Ch. 8.2",https://bradleyboehmke.github.io/HOML/bagging.html;https://bradleyboehmke.github.io/HOML/random-forest.html;https://bradleyboehmke.github.io/HOML/gbm.html; https://edld654-fall23.netlify.app/schedule.html; https://edld654-fall23.netlify.app/schedule.html,Assignment 3,https://www.kaggle.com/code/uocoeeds/assignment-3
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