## Materials

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Assignment0_data (2.7 MB, rev 1) | Assignment 0 - Notebooks and Data | |

Assignment0_handout (179 KB, rev 2) | Assignment 0 - Problem Set | |

Assignment0_solution_programming (2.5 MB, rev 1) | Assignment 0 - Sample solution practical problems | |

Assignment0_solution_theory (224 KB, rev 1) | Assignment 0 - Sample solution theoretical problems | |

Assignment1_data (5.9 KB, rev 1) | Assignment 1 - Notebooks and Data | |

Assignment1_handout (209 KB, rev 1) | Assignment 1 - Problem Set | |

Assignment2_data (4.4 MB, rev 1) | Assignment 2 - Notebooks and Data | |

Assignment2_handout (236 KB, rev 2) | Assignment 2 - Problem Set | |

Assignment3_data (20 KB, rev 1) | Assignment 3 - Notebooks and Data | |

Assignment3_handout (192 KB, rev 4) | Assignment 3 - Problem Set | |

Assignment4_data (229 KB, rev 1) | Assignment 4 - Notebooks and Data | |

Assignment4_handout (291 KB, rev 1) | Assignment 4 - Problem Set | |

Assignment5_data (17 KB, rev 2) | Assignment 3 - Notebooks and Data | |

Assignment5_handout (337 KB, rev 4) | Assignment 5 - Problem Set | |

Assignment6_data (89 KB, rev 1) | Assignment 6 - Notebooks and Data | |

Assignment6_handout (324 KB, rev 1) | Assignment 6 - Problem Set | |

code-of-conduct (63 KB, rev 1) | Code of Conduct |

Assignment1_classwork (151 KB, rev 3) | Tutorial exercise sheet for Assignment1 (ungraded) | |

Assignment2_classwork (176 KB, rev 2) | Tutorial exercise sheet for Assignment2 (ungraded) | |

Assignment3_classwork (145 KB, rev 1) | Tutorial exercise sheet for Assignment 3 (ungraded) | |

Assignment4_classwork (189 KB, rev 1) | Tutorial exercise sheet for Assignment 4 (ungraded) | |

Assignment5_classwork (460 KB, rev 2) | Tutorial exercise sheet for Assignment 5 (ungraded) | |

Assignment6_classwork (310 KB, rev 3) | Tutorial exercise sheet for Assignment6 (ungraded) |

Beyond Linear (439 KB, rev 1) | Lecture Recap for Beyond Linear | |

Bias & Variance (2.9 MB, rev 1) | Lecture Recap for Bias & Variance | |

Classification 1 (3.2 MB, rev 1) | Lecture Recap Classification 1 | |

Classification 2 (5.5 MB, rev 1) | Lecture Recap Classification 2 | |

Clustering (986 KB, rev 1) | Lecture Recap for Clustering | |

Dimensionality Reduction (1.6 MB, rev 1) | Lecture Recap for Dimensionality Reduction | |

Generalization (329 KB, rev 1) | Lecture Recap Generalization | |

LinearRegression 1 (1.6 MB, rev 1) | Lecture Recap for Linear Regression 1 | |

LinearRegression 2 (1.9 MB, rev 1) | Lecture Recap for Linear Regression 2 | |

Regularization (392 KB, rev 1) | Lecture Recap for Regularization | |

Support Vector Machines (474 KB, rev 2) | Lecture Recap for Support Vector Machines | |

Trees and Forests (301 KB, rev 1) | Lecture Recap for Trees and Forests |

00_slides (759 KB, rev 1) | Lecture 0 - Organisation - Slides | |

01_slides (25 MB, rev 2) | Lecture 1 - Bias and Variance - Slides | |

02_slides (4.1 MB, rev 1) | Lecture 2 - Linear Regression I - Slides | |

03_slides (3.1 MB, rev 1) | Lecture 3 - Linear Regression II - Slides | |

04_slides (3.7 MB, rev 2) | Lecture 4 - Classification I - Slides | |

05_slides (7.4 MB, rev 1) | Lecture 5 - Classification II - Slides | |

06_slides (15 MB, rev 1) | Lecture 6 - Generalization - Slides | |

07_slides (4.3 MB, rev 3) | Lecture 7 - Regularization - Slides | |

08_slides (1.4 MB, rev 1) | Lecture 8 - Beyond Linear - Slides | |

09_slides (3.8 MB, rev 1) | Lecture 9 - Dimensionality Reduction - Slides | |

10_slides (2.3 MB, rev 1) | Lecture 10 - Clustering - Slides | |

11_slides (1.5 MB, rev 1) | Lecture 11 - Trees and Forests - Slides | |

12_slides (1.7 MB, rev 1) | Lecture 12 - Support Vector Machines - Slides | |

13_slides (4.0 MB, rev 1) | Lecture 13 - Neural Networks - Slides | |

14_slides (1.3 MB, rev 1) | Lecture 14 - ML and the Real World - Slides |

eml23-exam1-questions (999 KB, rev 1) | EML 23 – Exam 1 – Questions | |

eml23-exam1-solutions (1.1 MB, rev 1) | EML 23 – Exam 1 – Answers |

An Introduction to Statistical Learning with Applications in Python | Main text book for the course | |

Fairness and Machine Learning | Optional Reading for Lecture 14 | |

The Algorithmic Foundations of Differential Privacy | Optional Reading for Lecture 14 | |

The Elements of Statistical Learning (2nd Edition) | Auxiliary text book for the course | |

The Sound of Machine Learning | The Sound of Machine Learning (that is just logistic regression) | |

Visualizing Data using t-SNE | Additional Reading for Lecture 9 – Dimensionality Reduction |

eml23-exam2-questions (964 KB, rev 1) | EML 23 – Exam 2 – Questions | |

eml23-exam2-solutions (1.2 MB, rev 2) | EML 23 – Exam 2 – Answers |

eml20-exam1-questions (266 KB, rev 1) | EML 20 – Exam 1 – Questions | |

eml20-exam1-solutions (285 KB, rev 1) | EML 20 – Exam 1 – Solutions | |

eml20-exam2-questions (294 KB, rev 1) | EML 20 – Exam 2 – Questions | |

eml20-exam2-solutions (420 KB, rev 1) | EML 20 – Exam 2 – Solutions | |

eml21-exam1-questions (297 KB, rev 1) | EML 21 – Exam 1 – Questions | |

eml21-exam1-solutions (411 KB, rev 2) | EML 21 – Exam 1 – Solutions | |

eml21-exam2-questions (507 KB, rev 1) | EML 21 – Exam 2 – Questions | |

eml21-exam2-solutions (656 KB, rev 1) | EML 21 – Exam 2 – Solutions | |

eml22-exam1-questions (404 KB, rev 1) | EML 22 – Exam 1 – Questions | |

eml22-exam1-solutions (376 KB, rev 1) | EML 22 – Exam 1 – Solutions | |

eml22-exam2-questions (682 KB, rev 1) | EML 22 – Exam 2 – Questions | |

eml22-exam2-solutions (783 KB, rev 1) | EML 22 – Exam 2 – Solutions |

icon_pdf (7.7 KB, rev 1) | Icon for links to PDF files | |

icon_youtube (7.7 KB, rev 1) | Icon for links to YouTube | |

icon_zoom (1.3 KB, rev 1) | Icon for links to Zoom meetings |

All electronic documents for this lecture are made available exclusively for your studies and must not be forwarded, nor reproduced, nor used in other documents. Individual figures may originate from copyrighted sources even when not explicitly designated as such.