Mathematics for machine learning

A dinosaur
LecturerBachmann, Henrik, Associate Professor
DepartmentG30, 2020 Fall
Recommended for:Undergraduate students majoring in science
Tags
  • machine learning
  • Course Purpose

    The purpose of this lecture is to give a basic mathematical introduction to machine learning.

    Content

    1. Overview of machine learning
    2. (Linear) Regression
    3. Review Linear Algebra
    4. Programming & doing mathematics in Python
    5. Introduction to Probability
    6. Support vector machines
    7. k-means clustering
    8. Neural networks
    9. Deep learning

    Course Prerequisites

    Basic knowledge in Linear Algebra and Calculus is helpful. We will also do some programming in Python. Programming knowledge are useful but not necessary since a rough introduction to programming in Python will be part of the course. Motivated 1st-year students can also attend without these prerequisites if they contact the lecturer beforehand. Due to the programming part of the lecture, students should have (access to) a computer/laptop.

    Additional information

    Detailed homepages for this course including materials and videos.


    クリエイティブ・コモンズ・ライセンス
    This lecture is provided under Creative Commons Attribution-Non Commercial-ShareAlike 4.0 International.


    Last updated

    September 22, 2022