Lectures and Material

Here you can find information on the lectures, as well as information on when you should have enough information to be able to attempt the various practical and theoretical python notebook assignments. The project should be started as soon as possible, and you should try different machine learning algorithms on the data set as you learn them in the course.

There is a menu item for each lecture where you can find a reading guide for the textbook, links to additional material and links to slides.

Please note that the dates still could change, and the dates in timeedit are always correct.

You should now have all the knowledge you need to attempt practical notebooks 1 and 2 (P1 & P2)

You now should have the knowledge to attempt the the first theoretical notebook (T1).

You should now be able to attempt the second theoretical notebook (T2) and the third practical notebook (P3).

You should now be able to attempt the final practical notebook (P4).

You should now be able to attempt theoretical notebook 3 (T3) and (T4)

Practical and Theoretical Notebooks

There are 4 practical and theoretical notebooks that are done individually as assignments. Links will be provided but the topics covered in the notebooks are as follows.

Practical Notebooks

• P1 : Basic programming with Python, lists, sets and an introduction to NumPy.
• P2 : Introduction to Pandas
• P3 : Linear and Logistic regression with scikit-learn
• P4 : Preprocessing, feature engineering and cross validation.

Theoretical Notebooks

• T1 - Logistic Regression
• T2 - Using naive Bayes for classifying tweets.
• T3 - Using K-means classifiers
• T4 - The Entropy of the normal distribution and binary decision trees using ID3.

Deadlines

These should be the same deadlines as are in Studium. If there is any discrepancy then please inform me.