_site.yml

name: "EDLD 654 - UO COE"

title: "Applied Machine Learning for Educational Data Science"

description: |

  This course focuses on applied machine learning (ML), with an emphasis on 
  supervised learning methods that have emerged over the last several decades.
  
  The primary goal of these methods is to create models capable of making
  accurate predictions, which generally implies less emphasis on statistical 
  inference.
 
base_url: https://edld654-fall22.netlify.app/

google_analytics: "G-1RQZJC7E05"

output_dir: "_site"

creative_commons: CC BY

navbar:
  right:
    - text: "Home"
      href: index.html
    
    - text: "Syllabus"
      href: syllabus.html
      
    - text: "Schedule"
      href: schedule.html
    
    - text: "Lecture Notes"
      menu:
        - text: "Lecture-1"
          href: lecture-1.html
          
        - text: "Lecture-2a"
          href: lecture-2a.html
          
        - text: "Lecture-2b"
          href: lecture-2b.html
        
        - text: "Lecture-3a"
          href: lecture-3a.html
          
        - text: "Lecture-3b"
          href: lecture-3b.html
          
        - text: "Lecture-4"
          href: lecture-4.html
          
        - text: "Lecture-5"
          href: lecture-5.html

        - text: "Lecture-6a"
          href: lecture-6a.html
          
        - text: "Lecture-6b"
          href: lecture-6b.html
          
        - text: "Lecture-7a"
          href: lecture-7a.html
          
        - text: "Lecture-7b"
          href: lecture-7b.html  
          
    - text: "Kaggle Notebooks"
      menu:
        - text: "Notebook-1"
          href: https://www.kaggle.com/code/uocoeeds/week-1-introduction/notebook
        
        - text: "Notebook-2a"
          href: https://www.kaggle.com/code/uocoeeds/lecture-2a-data-preprocessing-i
        
        - text: "Notebook-2b"
          href: https://www.kaggle.com/code/uocoeeds/lecture-2b-data-preprocessing-ii
          
        - text: "The {recipes} demo"
          href: https://www.kaggle.com/code/uocoeeds/the-recipes-package-demo
          
        - text: "Notebook-3a"
          href: https://www.kaggle.com/code/uocoeeds/lecture-3a-overview-of-linear-regression
          
        - text: "Notebook-3b"
          href: https://www.kaggle.com/code/uocoeeds/lecture-3b-review-of-bias-variance-tradeoff
          
        - text: "Building Linear Models with `caret`"
          href: https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-with-cross-validation

        - text: "Notebook-4"
          href: https://www.kaggle.com/code/uocoeeds/lecture-4-regularized-penalized-regression
          
        - text: "Building a Ridge Regression Model"
          href: https://www.kaggle.com/code/uocoeeds/building-a-ridge-regression-model
          
        - text: "Building a Lasso Regression Model"
          href: https://www.kaggle.com/code/uocoeeds/building-a-lasso-regression-model
          
        - text: "Building an Elastic Net Model"
          href: https://www.kaggle.com/code/uocoeeds/building-a-regression-model-with-elastic-net
          
        - text: "Using the Existing Models to Predict Outcome for New Observations"
          href: https://www.kaggle.com/code/uocoeeds/using-the-prediction-models-for-a-new-text

        - text: "Building a Logistic Regression Model"
          href: https://www.kaggle.com/code/uocoeeds/building-a-logistic-regression-model/notebook
          
        - text: "Building a Classification Model with Ridge Penalty"
          href: https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-ridge-penalty/

        - text: "Building a Classification Model with Lasso Penalty"
          href: https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-lasso-penalty
           
        - text: "Building a Classification Model with Elastic Net"
          href: https://www.kaggle.com/code/uocoeeds/building-a-classification-model-with-elastic-net
 
        - text: "Building a Prediction Model with K-nearest neighbors"
          href: https://www.kaggle.com/code/uocoeeds/building-a-prediction-model-using-knn

        - text: "Building a Classification Model with K-nearest neighbors"
          href: https://www.kaggle.com/code/uocoeeds/building-a-classification-model-using-knn
          
        - text: "Building a Prediction Model with a Decision Tree"
          href: https://www.kaggle.com/uocoeeds/building-a-prediction-model-using-a-decision-tree

        - text: "Building a Classification Model with a Decision Tree"
          href: https://www.kaggle.com/code/uocoeeds/building-a-classification-tree-algorithm
          
           
    - text: "Slides"
      menu:
        - text: "Week 1"
          href: slides/slide1.html
        
        - text: "Week 2"
          href: slides/slide2.html
          
        - text: "Week 3 & 4"
          href: slides/slide3.html
          
        - text: "Week 5"
          href: slides/slide4.html
 
        - text: "Week 6 & 7"
          href: slides/slide5.html

        - text: "Week 8_Part 1"
          href: slides/slide6a.html 
        
        - text: "Week 8_Part 2"
          href: slides/slide6b.html 
          
    - text: "Assignments"
      menu:
        - text: "Assignment1"
          href: https://www.kaggle.com/code/uocoeeds/assignment-1
          
        - text: "Assignment2"
          href: https://www.kaggle.com/code/uocoeeds/assignment-2

        - text: "Assignment3"
          href: https://www.kaggle.com/code/uocoeeds/assignment-3
          
    - icon: fa fa-github
      href: https://github.com/uo-datasci-specialization/c4-ml-fall-2022

output: 
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