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INF2-FDS: S1 Week 6 - Linear models

Lecture notes - Linear models (chapter 10)

Slides - The slides for Monday and Wednesday are here.

Jupyter notebook and data - To produce the figures in the lectures notes and slides. Download here.

Comprehension questions on regression - Access on Gradescope via Learn, the solutions are here.

Lab: On k-Nearest Neighbours, last week's lecture topic.

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  • INF2-FDS: S1 Week 6 Lab

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  • INF2-FDS: Course team
  • INF2-FDS: Schedule
  • INF2-FDS: Course Materials
    • INF2-FDS: S1 Week 1 - Introduction and Data
    • INF2-FDS: S1 Week 2 - Descriptive statistics
    • INF2-FDS: S1 Week 3 - Exploratory data analysis, data communication and visualisation
    • INF2-FDS: S1 Week 4 - Data Ethics, Data Scraping and statistical relationships
    • INF2-FDS: S1 Week 5 - Introduction to supervised learning - Nearest Neighbours
    • INF2-FDS: S1 Week 6 - Linear models
      • INF2-FDS: S1 Week 6 Lab
    • INF2-FDS: S1 Week 7 - Coursework 1
    • INF2-FDS: S1 Week 8 - Multiple regression
    • INF2-FDS: S1 Week 9 - Dealing with high dimensions
    • INF2-FDS: S1 Week 10 - Randomness, sampling and simulation
    • INF2-FDS: S1 Week 11 - Estimation and confidence intervals
    • INF2-FDS: S2 Week 1 - Hypothesis testing and A/B testing
    • INF2-FDS: S2 Week 2 - Logistic regression
    • INF2-FDS: S2 Week 3 - Introduction to unsupervised learning - K-means
    • INF2-FDS: S2 Week 4 - Ethical and legal issues in supervised learning
    • INF2-FDS: S2 Week 5 - Regression and inference
    • INF2-FDS: S2 Week 6 - Software engineering for data science
    • INF2-FDS: S2 Week 8 Project writing workshop
    • INF2-FDS: Revision
  • INF2-FDS: Labs
  • INF2-FDS: Workshops
  • INF2-FDS: Resource List
  • INF2-FDS: Assessment
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