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Introduction to Machine Learning

Course description

Requirements:

Programming skills with Python, e.g. course Introduction to Python. Basic understanding of statistical methods, in particular regression analysis, is recommended, e.g. course Introduction to Statistics.

Course overview:

In the Introduction to Machine Learning course, we delve into the practical application of machine learning techniques for data analysis using Python. This course is designed for individuals interested in leveraging machine learning for data analysis, focusing less on traditional statistics and more on predictive modeling.

By the end of the course, you will be able to confidently select and utilize suitable machine learning techniques, interpret your findings effectively, and apply them to real-world scenarios. Additionally, you'll learn concepts and acquire practical skills that you can translate into solutions for your own research challenges. 

    Topics:

    • Introduction to Machine Learning
    • Linear Regression
    • Classification Algorithms
      • Logistic regression
      • K-nearest neighbors
      • Decision Trees
    • Model Evaluation and Selection
      • Cross-validation
      • Performance metrics

    Differences from "Introduction to Statistics" course:

    • Emphasis on predictive modeling rather than statistical inference.
    • Focus on understanding key AI terminology and practical applications.
    • Helmholtz Munich doctoral researchers cannot replace the mandatory course "Introduction to Statistics" with this course.

    Target Audience:

    • Individuals keen on analyzing data in Python, particularly those interested in machine learning techniques.
    • Participants should have some basic knowledge of statistics.
    • Familiarity with Python programming is required.

    Methods:

    The course consists of theoretical lessons on machine learning tools, how to apply maschine learning teachniques and how to evaluate the results. Theoretical lessons will be followed by hands-on examples with best-practice solutions in Python.

    Format:

    • Duration: 2 Days
    • Language: English
    • This course will be offered either on campus (in person), or online
    • For online courses we use the software Zoom.

    Dates and Application:

    • Courses provided for Helmholtz Munich:
      • You can check the current dates and whether the courses are already fully booked here*.
      • Please read the corresponding FAQ* before applying via the forms of the HR Development department*.

     * Links marked with * are only available for Helmholtz Munich staff.