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CAS Machine Learning

This program is also available in German

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  1. Computer Science Computer Science
  2. Continuing Education Continuing Education
  3. CAS Machine Learning CAS Machine Learning

CAS Machine Learning Using machine learning effectively in your day-to-day work

Machine learning (ML) is transforming the world. It is currently the departure point for the development of AI systems. Neural network models are capable of learning from enormous data volumes and of executing cognitive tasks including language translation, medical diagnoses and market forecasts. In this continuing education program, you will learn how this technology works and how you can use it to address real-life problems.

CAS Machine Learning

This program is also available in German

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The program at a glance

The basic principle of machine learning (ML) is the teaching of computers to learn from data, enabling them to execute useful tasks. There are many types of data, including images, text, tables and audio recordings. The tasks to be executed are as manifold and range from the classification of documents to risk assessment, to process optimization, to computer vision and to recommender systems. In this course, you will learn

  • which ML models are the right ones for a host of tasks,
  • how you can prepare your data to train these models,
  • to train your ML system, to evaluate its performance and to scale the system to operate it productively. 

After a series of remarkable breakthroughs in the past decade, we now have machine learning models that perform at an astonishing level. ML with many adjustable parameters (such as deep learning models) allow for the development of human-like chatbots and self-driving cars. The program teaches the neural network models on which these systems are based as well as other ML approaches such as decision trees and Bayesian learning.

The CAS in Machine Learning program is for everybody wishing to develop a deeper understanding of machine learning and to improve their skills in using it. Programming experience is welcome, but not required. To support participants who did not do much programming in recent years, the first module is fully dedicated to brushing up coding skills with Python. If you have any question concerning the admission requirements, please contact the head of program.

The course objective is to enable you to use ML systems directly in your day-to-day work environment. It will give you a valuable and sought-after professional qualification. 

More information

Module overview 

Module 1 - Introduction 

  • Math refresher: Linear algebra, calculus, statistics
  • Coding refresher: Python, Numpy, Matplotlib 
  • The history and development of machine learning
  • Data management and feature engineering

Module 2 - Machine Learning

  • Unsupervised learning
  • Supervised learning
  • Artificial neural networks
  • Model validation
  • Model diagnostics

Module 3 – Deep Learning

  • Convolutional neural networks 
  • Computer vision
  • Generative models
  • Artificial neural networks
  • Natural language processing (NLP)
  • Transformers: Attention is all you need

Modul 4 – Other types of Machine Learning

  • Recommender systems
  • Decision trees, random forest and gradient boosting
  • Bayesian learning and Bayesian networks
  • Self-supervised learning
  • Reinforcement learning

Module 5 – Production deployment and MLops 

  • The workflow of machine learning
  • Model deployment in production
  • MLops concepts and strategies
  • Architectures of deployment: Edge, Cloud, Browser
  • Monitoring of production models

The focus is on practical work: each topic will be studied through Python programming and exercise units in groups. Guest lecturers will come in to discuss advanced topics. The requirement to obtain the CAS certificate is course attendance for the duration of the program.

Transfer project
In the transfer project, participants work with a real-life dataset. The process comprises the following stages:

  1. The participants either choose an existing project/problem from their professional practice or define one based on their personal interests.
  2. They discuss the idea with the lecturers, who directly approve it or suggest changes.
  3. During the program, the participants are given the time to work their project supervised by their lecturers.
  4. The participants present their projects on the final day of the CAS program.

The goal is for the participants to gain experience with a real-life project, creating added value for their CV and their companies alike.

Technologies used:

  • Python, Jupyter, Scikit Learn, Pandas, Numpy, Matplotlib 
  • Tensorflow, Keras
  • GPUS and hardware acceleration
  • AWS, MS-Azure, Google Cloud 

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Facts

Start of programme

October 20, 2023

End of programme

January 26, 2024 

Duration

5 months

Course costs

CHF 7'900.–

Registration fee and course materials are included. Pay course fees with Bitcoin: Message to +41 41 228 42 42 or to info@hslu.ch. 5 percent discount for alumni members of the Lucerne University of Applied Sciences and Arts. SVEB continuing education vouchers are accepted

Head of programme
  • Dr. Umberto Michelucci
Information events
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  • Thursday, 28 September 2023, Online
  • Tuesday, 3 October 2023, Online
  • Monday, 9 October 2023, Online
  • Tuesday, 10 October 2023, Online
  • Monday, 20 November 2023, Online
  • Monday, 11 December 2023, Online
  • Monday, 8 January 2024, Online
  • Monday, 12 February 2024, Online
  • Monday, 11 March 2024, Online
Degree

Certificate of Advanced Studies in Machine Learning from the Lucerne University of Applied Sciences & Arts

Programme type

CAS

ECTS

15

Tuition times

Friday and Saturday (15 days)

Language of instruction
  • German
  • English
Venue

Online
Rotkreuz

Contact hours

120

Target group

Executives and specialists from IT departments, project managers and consultants who are required to analyse data more intensively. The CAS is aimed at people who are interested in applying modern machine learning techniques to real and applied application cases.

Requirements

A degree at tertiary level (ETH/university, university of applied sciences, college of higher education and others) and at least two years of professional experience after graduation. Persons with an equivalent qualification and several years of professional experience may be admitted in limited numbers via a standardized admission procedure ("sur dossier") - this may be subject to conditions.

Some programming experience is advantageous.

Provider(s)

Computer Science and Information Technology

This programme is part of the following continuing education programmes

  • MAS Agile DevOps & Cloud Transformation

  • MAS Business Intelligence

  • MAS Digital Architect & Transformation

Dr. Umberto Michelucci

Programm Director

Show email

Melda Kahveci

Program Assistant

+41 41 349 31 39

Show email

Information events

  • Thursday, 28 September 2023, Online
  • Tuesday, 3 October 2023, Online
  • Monday, 9 October 2023, Online
  • Tuesday, 10 October 2023, Online
  • Monday, 20 November 2023, Online
  • more Information events

Further programs in English

  • CAS Digital Twins

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Contact

Logo Lucerne School of Computer Science and Information Technology

Lucerne School of Computer Science and Information Technology


Campus Zug-Rotkreuz
6343

+41 41 349 30 70

informatik@hslu.ch

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