Machine Learning

Course resources

• Link to shared Dropbox folder. Will contain solutions to exercises as they get released.

Sessions Info

2025-04-22, Tuesday

• Final exam will take place on Wednesday 2025-04-30, 15:00-18:00 at Room 401.

• Midterm grades are available in Moodle. Forecasting assignment grades will be available in the next couple of days.

• 2024 Final Exam Statement is available in Moodle

2025-04-10, Thursday

• Easter reading suggestions (looking beyond this course):
  • Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) by Brandon Rohrer
  • The material in this slides (from IMAT forecasting lectures)
  • Any chapter from Deep Learning: A Visual Approach, by Andrew Glassner
• Remember the forecasting submission date is Tomorrow

2025-04-08, Tuesday

• ARMA model slides. Identification plots, pages 28-36.

2025-04-03, Thursday

• Last forecasting session: with today’s notebook you have all the ingredients to complete the assignment.

2025-04-01, Tuesday

• A suggested reading about the state of forecasting: Time-Series Forecasting: Deep Learning vs Statistics — Who Wins? by Nikos Kafritsas in Medium
• All the forecasting theory notebooks will be available tomorrow.

2025-03-27, Thursday

• The statement of the forecasting assignment will be posted to Moodle tomorrow Friday around noon.
• For the next sessions we recommend reading Forecasting with sktime

2025-03-25, Tuesday

• Notebook for 4_3_Baseline_Arima released.

2025-03-20, Thursday

• Python books

2025-03-18, Tuesday

• Link to fpp3

2025-03-11, Tuesday

• Notebook for 3.3_Beyond_Linear_Models is updated. Make sure you review it thoroughly!

2025-02-27, Thursday

• Notebook for 3.3_Beyond_Linear_Models is provisional and only available at Dropbox.

2025-02-25, Tuesday

• Prepare Screen Recording Software: suggested OBS configured as described in this guide. But you can use any software you like, as long as you make sure that a 2h recording fits in your drive, it does not impact too much on your laptop performance and the text in the screen capture can be read.

2025-02-20, Thursday

• Our last 3h. session!
• Midterm 2024 published to Moodle.
• Prepare Screen Recording Software (details coming soon)
• Book recommendations:
  • Probabilistic Machine Learning (Volume 1) by K.P. Murphy
  • Probabilistic Machine Learning (Volume 2) by K.P. Murphy.
    Both books are freely available online (grab them!)

2025-02-18, Tuesday

• Book recommendations:
  • Deep Learning: A Visual Approach, by Andrew Glassner
  • Grokking Machine Learning, by Luis G. Serrano
• Paper: Support Vector Machines: Hype or Hallelujah?

2025-02-13, Thursday

• Remember: 3h. today, but this time the last one also in this room!
• Are this news good news? Enlace al informe de IndesIA
• Paper on Machine Learning models comparison (no deep learning)

2025-02-11, Tuesday

• From Kunapuli (2023),Taxonomy of Ensemble Methods
• Kaggle
• Cross Validated

2025-02-06, Thursday.

• Remember: 3h. today, last one at room 112.
• Assignment 1 due date: Friday 2025-02-14
• 2_3_KNN_Validation_Extended.html

2025-02-04, Tuesday.

• Additional script for today’s session
• Yellowbrick

2025-01-30, Thursday.

• Remember: 3h. today, last one at room 112.
• Assignment 1 due date: Friday 2025-02-14

Suggested Readings and Links

• Python Data Cleaning Cookbook
• machinelearningmastery.com: How to Remove Outliers for Machine Learning

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MLMIC25 Sessions

Regression

• Chapter 2, Part 1 : The Classification Problem. EDA and Preprocessing
• Chapter 3, Part 1: Introduction to Regression

Forecasting

• Chapter 4, Part 1: Introduction to Forecasting

• Chapter 4, Part 2: Stationarity

• Chapter 4, Part 3: Baseline and ARIMA models

References

Kunapuli, Gautam. 2023. Ensemble Methods for Machine Learning. Manning Publications. https://www.manning.com/books/ensemble-methods-for-machine-learning.