Skip navigation Jump to main navigation

Spring 2021 Advisory

Find the latest information SPS's plans for the Spring and University resources. Message from the Dean.
Close alert

The Importance of Teaching Machine Learning

Francesca Lazzeri, Ph.D. is an experienced scientist and machine learning practitioner with over 12 years of both academic and industry experience. She is author of the book Machine Learning for Time Series Forecasting with Python (2020, Wiley) and many other publications, including technology journals and conferences. 

Artificial intelligence (AI) has shaped the development of new products and services: from self-driving cars and smart electrical grids to voice assistants that power speakers and high-tech coffee pots, these technologies are mainstays of life. Many industries are currently building even more robust machine learning pipelines capable of analyzing larger data sets while delivering faster, more accurate results on vast scales. 

As a consequence, the demand for expertise in AI and machine learning is growing rapidly. Machine learning concepts like computer vision quickly open doors to some of today’s most exciting career opportunities for forward-thinking technology professionals. In my course Introduction to Artificial Intelligence with Python, students learn how to take the first step toward building AI driven applications. This course aims at teaching the most important concepts of the machine learning workflow that student developers and data scientists will follow to build end-to-end AI  solutions. 

What is machine learning and what is AI?

Machine learning is a subclass of AI that seeks to answer the question: “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”. Machine learning models learn, identify patterns, and make decisions with minimal intervention from humans.

Machine learning models learn, identify patterns, and make decisions with minimal intervention from humans.

AI is a technique that enables computers to mimic human intelligence. By using machine learning techniques, data scientists and engineers can build AI applications that do tasks that are commonly associated with human intelligence.

Why is it important to teach machine learning?

1. Career Opportunities

AI and machine learning jobs are projected to be worth almost $31 billion by 2024. Industries that are already using AI and machine learning heavily include healthcare, education, marketing, retail and ecommerce, and financial services. Pursuing a machine learning career is a solid choice for a professional role that will be in demand for decades.

AI and machine learning jobs are projected to be worth almost $31 billion by 2024.

Those statistics also underscore the need for building machine learning talent: the demand for qualified AI professionals already surpasses that of data scientists, and this trend is expected to continue for the next few decades.  Many companies see AI not just as an enhancer of productivity, but also as a factor of production. AI will not only boost growth by augmenting human labor, but also because of its potential to create new goods, services, and innovations. Machine learning’s growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems.

2. Solve some of the world’s most complex challenges 

Today’s students are the innovators and inventors of the future who can use technology to help find solutions to the types of problems we’re facing today. Educators are key enablers of this ability. AI has also the potential to help society overcome some of its most challenging issues such as reducing poverty, improving education, delivering healthcare, and eradicating rare diseases.

Today’s students are the innovators and inventors of the future who can use technology to help find solutions to the types of problems we’re facing today.

Another field where AI can have a significant positive impact is in serving the more than 1 billion people in the world with disabilities. One example of how AI can make a difference is an app called Seeing AI, that can assist people with blindness and low vision as they navigate daily life. Seeing AI was developed by a team that included a Microsoft engineer who lost his sight at 7 years of age. This powerful app proves the potential for AI to empower people with disabilities by collecting images from the user’s surroundings and describing what is happening around them.

3. Advance diversity and inclusion

AI will increase the need to solve one of the most crucial societal challenges nowadays: advancing diversity and inclusion in our society. The​ ​threat​ ​of​ ​bias​ ​rises​ ​when​ AI​ ​systems​ ​are​ ​applied ​to​ ​critical​ ​societal areas ​like​​ healthcare and education.​ While​ ​all​ ​possible​ ​consequences ​of​ ​such​ ​biases​ ​are​ ​worrying,​ ​finding pragmatic solutions​ ​can be a very complex process.​ 

​Biased​ ​AI​ ​can​ ​be the result of​​ ​many different ​factors,​ ​for example what​ ​goals​ ​AI​ ​developers​ ​have​ ​in​ ​mind during​ ​development ​and​ ​whether​ ​the​ ​​​systems​ ​developed are representative enough of ​different​ ​parts​ ​of​ ​the​ ​population. ​ ​Most importantly, AI​ ​solutions​ ​learn ​from​ ​training​ ​data. ​Training​ ​data​ ​can​ ​be imperfect, skewed, often​ ​drawing​ ​on​ ​incomplete​​ ​samples​ ​that​ ​are​ ​poorly​ ​defined​ ​before​ ​use. ​​Additionally, ​because of necessary ​labelling and feature engineering processes, ​human biases​ ​and​ ​cultural​ ​assumptions​ ​can also be​ ​transmitted​ ​by​ ​classification​ ​choices. ​All these technical challenges can result in the​ ​exclusion​ ​of​ ​sub-populations​ ​from​ ​what​ ​AI​ ​is​ ​able​ ​to​ ​see and​ learn from.

Sources

Columbus, Louis. (2020). Roundup Of Machine Learning Forecasts And Market Estimates, 2020. https://www.forbes.com/sites/louiscolumbus/2020/01/19/roundup-of-machine-learning-forecasts-and-market-estimates-2020/

Lazzeri, Francesca. (2018). Mind Bytes: Solving Societal Challenges with Artificial Intelligence. https://medium.com/@francescalazzeri/mind-bytes-solving-societal-challenges-with-artificial-intelligence-a5041e670d74

Lazzeri, Francesca. (2019). Deep learning vs. machine learning in Azure Machine Learning. www.aka.ms/deeplearningvsmachinelearning 

Mitchell, Tom. (2006). The Discipline of Machine Learning. http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf 

Yarkoni, Charlotte. (2020). Skills for the future start today. https://blogs.microsoft.com/blog/2020/05/14/skills-for-the-future-start-today-new-resources-for-student/ 

The views expressed are those of the author and do not necessarily represent the views of any other person or entity.

Authors