Applied Machine Learning – a new course for master’s students of F7 Computer Engineering
Assistant of Design Automation Department, Candidate of Technical Sciences Ivan Khakhanov began developing a course on applied machine learning, which will be taught to master’s students within the framework of the OPP “Specialized Computer Systems”, specialty F7 Computer Engineering, 2025 enrollment, which is published for discussion on the university website.
>>>Link to the university website: Study programme Specialised computer systems<<<
Lectures that demonstrate achievements in the application of machine learning algorithms for analyzing large amounts of data and optimizing processes in various fields are available to everyone on the YouTube channel:
Machine learning is relevant for achieving the UN Sustainable Development Goals (SDGs) by providing tools for data analysis, forecasting, and process optimization in various industries. Here are some examples of how machine learning contributes to the SDGs.
SDG 2 Sustainable agriculture: optimizing resource use – machine learning helps to optimize the use of water, fertilizers, and pesticides in agriculture, increasing yields and reducing negative environmental impact; yield forecasting – machine learning algorithms can predict crop yields, which helps to plan food production and distribution.
SDG 3 Healthcare: Disease diagnosis – machine learning algorithms can analyze medical images and data to diagnose diseases at early stages; drug development – machine learning helps speed up the development of new drugs and treatments.
SDG 4 Quality education: personalization of learning – machine learning algorithms can analyze student performance data to personalize learning and increase its effectiveness; accessibility of education – machine learning helps to create educational resources accessible to people with disabilities.
SDG 7 Clean energy: optimizing the performance of renewable energy sources – machine learning algorithms can optimize the performance of renewable energy sources such as solar and wind power plants, increasing their efficiency; energy production forecasting – machine learning helps to predict the production of energy from renewable sources, which helps to plan energy supply.
SDG 11 Sustainable cities and communities: optimizing traffic flows – machine learning helps to optimize traffic flows in cities, reducing congestion and emissions; waste management – machine learning algorithms can optimize waste collection and recycling, reducing its negative impact on the environment.
SDG 13 Combat climate change: predicting climate change – machine learning models analyze large amounts of climate data to predict future climate change, which helps to make informed decisions on adaptation and mitigation; optimizing energy consumption – machine learning algorithms can optimize energy consumption in buildings, industrial processes, and transportation systems, reducing greenhouse gas emissions; monitoring deforestation and natural disasters – machine learning helps to analyze satellite data to determine whether a country is experiencing a significant increase in climate change.
These are just a few examples of how machine learning contributes to the SDGs. As technology advances, machine learning will play an increasingly important role in solving global problems and creating a sustainable future.
We invite everyone interested to watch the course presentation on our YouTube channel and join the discussion of this important research.
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