190.013 Introduction to Machine Learning Lab (2SH P, SS)
This course accompanies the 190.012 Introduction to Machine Learning lecture.
Enrolling for this exercise is a highly recommended but not a requirement for passing the machine learning lecture.
Format, Location & Time
- Format: The course format is physical attendance. The lectures are going to be broadcasted via Webex too, but the focus will be given only to participants physically present.
- Location: HS Thermoprozesstechnik
- Broadcast: WEBEX
- Dates: Fridays 13:15-15:00
Lectures
- Lecture 1: Preliminaries and Introduction to Google Collab and Python [grades]
- Lecture 2: Programming in Python, Probability Theory & Linear Algebra operations
- python classes
- gaussian distributions
- sampling
- plotting
- Linear Algebra
- Assignment 3: Linear Probabilistic Regression
- features
- least-squares regression derivation & implementation
- regularization
- Assignment 4: Nonlinear Probabilistic Regression
- gaussian processes implementation
- kernels
- predictions
- Assignment 5: Probabilistic Time-Series-Models
- implementation & learning from real-world data
- visualization
- predictions
Details to the grading will be presented in the first exercise on the 03.03.2023. For each assignment code templates will be provided.
Learning objectives / qualifications
Hands-on experience with machine learning methods.
Course Resources
- Python Programming Tutorial by Socratica [recommended]
- MUOnline course link
- Latex Template for the Assignments
- Introduction to LaTex
- Introduction to Python
- Multi-Threading Jupiter Tutorial by Vedant Dave