Machine Learning lectures

Here is the material for a course of two-weeks I will be giving in a Master of Data Science and AI in Milan.

This is part of a series of other lectures modules on

1. Introduction to Data Science 📈
2. Deep Learning 🦾
3. Time Series ⌛
4. Computer Vision Hands-On 🕶️
5. Recommender Systems 🚀

Introduction

The lectures are devoted to students having little knowledge of python, but being aware of linear algebra and basic calculus.

The theaching philosophy is the following. I prefered to give a short theoretical introduction, then share practical code implementations and give students a sense of the “whole game” before delving into lower-level details.

Theory is really important to become a self-confident and solid data scientist, but to start and to not make people scared I was looking for a compromise between a complete bottom-up approach and the first principle derivation.

I am really eager to get your comments and suggestions, so please do not be shy and tell me what you think and where I can improve.

Install requirements

As usual, it is advisable to create a virtual environment to isolate dependencies. One can follow this guide and the suitable section according to the OS.

Once the virtual environment has been set up, one has to run the following instruction from a command line

pip install -r requirements.txt

This installs all the packages the code in this repository needs.

Interact with notebooks

You can use Binder, to interact with notebooks and play with the code and the exercises.

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Your lecturer 👨‍🏫

Oscar de Felice

I am a theoretical physicist, a passionate programmer and an AI curious.

I write medium articles (with very little amount of regularity), you can read them here. I also have a github profile where I store my personal open-source projects.

📫 Reach me!

Buy me a coffee ☕️

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