Summer
Course name Fundamentals of machine learning
Course date 13.05.2024 - 31.07.2024
Institution HAMK University of Applied Sciences
Course language English
Credits 5 ECTS credit

Field Natural Sciences HUB
Teacher Mazhar Mohsin
Email
Available for open UAS No
Level Bachelor

Queries related to enrolment practices
Enrolment period 11.03.2024 - 19.04.2024
Implementation plan
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Course enrolment info

Fundamentals of machine learning

13.05.2024 - 31.07.2024

Course description

The student:

  • is familiar with the machine learning engineer role of information systems in industry.
  • understands the features of machine learning to apply on real world problems.
  • understands the mathematical foundations behind the machine learning algorithms as well as the paradigms supervised and unsupervised learning.
  • is able to choose and tune the appropriate machine learning models on existing real life problems.
  • possesses skills in using the off-the-shelf machine learning tools. Designing timely and efficient algorithms in a range of real-world applications.
  • understanding of the strengths and weaknesses of many popular machine learning approaches.
  • is able to design and implement various machine learning algorithms in a range of real-world applications.
Main contents of the course:
  • Supervised learning, knn algorithm as an example
  • Unsupervised learning, k-means algorithm as an example
  • Quantitative variables/data, standard deviation, covariance, correlation
  • Linear Regression
  • Topic detection, regular expressions
  • Natural Language Processing – Sentiment Analysis
The course incorporates several practice assignments and a project completed individually. Before completing these projects, students will attend lectures on Zoom or watch videos focused on machine learning techniques and the use of Python libraries, complemented by small exercises to solidify their understanding. This approach ensures that students are well-prepared to tackle the practical challenges posed by the assignments and project. This course offers a comprehensive introduction to the fundamental concepts, methodologies, and tools essential for gaining a solid understanding of machine learning. Through this course, students will become acquainted with the critical role of Machine Learning Engineers in industry settings. By the end of this course, participants will be equipped to:
  • Recognize and articulate the core responsibilities and expertise necessary for a Machine Learning Engineer.
  • Understand and apply the key features of machine learning to tackle real-world problems.
  • Grasp the mathematical underpinnings of machine learning algorithms, including both supervised and unsupervised learning paradigms.
  • Select and fine-tune suitable machine learning models for practical applications.
  • Acquire proficiency in utilizing readily available machine learning tools and designing efficient, timely algorithms for diverse real-world scenarios.
  • Evaluate the advantages and limitations of various well-known machine learning strategies.
  • Design and execute various machine learning algorithms tailored to specific real-world applications.

Through a combination of online zoom sessions, video lectures, hands-on labs, and project-based learning, students will explore topics such as:

  • Supervised and unsupervised learning
  • Quantitative analysis (Quantitative variables/data, standard deviation, covariance, correlation)
  • Linear regression
  • Topic detection
  • Natural language processing
  • Sentiment analysis.

This curriculum is designed to not only provide theoretical knowledge but also to offer practical experience in applying machine learning techniques to real-life challenges.

 

Assessment criteria

1 – 5

Course way of working and time table

The course incorporates several practice assignments and a project completed individually. Before completing these projects, students will attend lectures on Zoom or watch videos focused on machine learning techniques and the use of Python libraries, complemented by small exercises to solidify their understanding. This approach ensures that students are well-prepared to tackle the practical challenges posed by the assignments and project. This course is a combination of online zoom sessions, video lectures, hands-on labs, and project-based learning conducted as online studies. Studying is done independently based on the materials provided in Moodle and based on other provided materials (recommented literature provided in the implementation plan)

Course info

The course utilizes Moodle/Learn as the primary learning management system, with Zoom for live communications and discussions. For implementing assignments and projects, proficiency in Python within a Jupyter Notebook or Visual Studio Code (VSCode) environment is required.

A list of recommended textbooks and software tools to support your learning journey is provided in the implementation plan.

Course additional info

  • Enroll in the course primarily through your own Peppi/Pakki/student desktop. If your home UAS is not yet part of the cross-study system Ripa, you can register using the enrollment form that opens from the button at the top of the page
  • You can find more information about enrollment on the Cross-institutional studies
  • Ask for more information on enrollment or course approval, studentservices@hamk.fi

Course enrolment info

  • Check the implementation plan before enrolling!
  • Centria, Humak, TAMK, Turku UAS, VAMK and XAMK student, enroll at Peppi/Pakki
    • you can see the enrollment status on your PSP.
    • you can find more information about enrollment on the Cross-institutional studies website
  • Student from another UAS (if using Peppi/Pakki for cross-study enrollment is not possible)
    • Select your home UAS from the list. If it is not there, you cannot register using the form.
    • Write your personal identity number in the correct format on the form! An incompletely reported ID may prevent you from accessing the course.
    • Fill in the email address of your home university on the enrolment form – entering a different email address does not entitle you to a study place! Make sure your address is spelled correctly.
    • Make a note for yourself of which course you have enrolled for.
    • Registration is binding. If you must cancel your registration, make room for another student by sending email to studentservices@hamk.fi
  • Enrollments will be processed at once after the enrollment period has ended.
  • You will receive an email when you have been approved and an ID has been created for HAMK’s information systems. Email sometimes goes into spam, so check your spam as well.
  • Activate the HAMK user ID! We require the registration and use of HAMK IDs during the course.