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
- Assignment 1: Preliminaries and Introduction to Google Collab and Python
- Assignment 2: Programming in Python, Probability Theory & Linear Algebra operations
- python classes
- gaussian distributions
- Linear Algebra
- Assignment 3: Probability Theory
- Classes for Distributions
- Plotting & Sampling
- Bayes Theorem
- Assignment 4: Linear Regression with Diabetes Dataset
- Data Preprocessing
- Least Squares Regression
- Ridge Regression
- Lasso Regression
- Assignment 5: Non-Linear Regression & Classification
- Non-linear Feature Transformations (Polynomial and RBFs)
- Neural Network
- Assignment 6: Probabilistic Time Series Models ( Gaussian Process)
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.
- 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
Python Videolectures and Tutorials
Fotios Lygerakis via email at email@example.com