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Updated in [June 30th, 2023]

This course provides an introduction to Machine Learning, a field of study that gives computers the ability to learn without being explicitly programmed. Students will learn the models and methods used in Machine Learning and apply them to real-world situations. Major perspectives covered include probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning. Topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection. Methods include linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, and Gaussian mixture models. In the first half of the course, students will cover supervised learning techniques for regression and classification. In the second half, students will shift to unsupervised learning techniques, including data clustering, matrix factorization, and sequential models for order-dependent data.

[Applications]
After this course, students can apply their knowledge of Machine Learning to a variety of real-world problems. These include predicting outputs based on inputs, clustering data, factorizing matrices, and creating models for order-dependent data. Students can also use their knowledge to develop recommendation engines, rank sports teams, and plot the path of movie zombies.

[Career Path]
Job Position Path: Machine Learning Engineer
A Machine Learning Engineer is a professional who applies their knowledge of machine learning techniques to develop and deploy machine learning models. They are responsible for designing, developing, testing, and deploying machine learning models that can be used to solve real-world problems. They must have a strong understanding of the fundamentals of machine learning, including supervised and unsupervised learning, as well as the ability to develop and deploy machine learning models. They must also have a strong understanding of the underlying algorithms and techniques used in machine learning, such as linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, and Gaussian mixture models.

The development trend of Machine Learning Engineer is very promising. With the increasing demand for data-driven decision making, the need for Machine Learning Engineers is expected to grow significantly in the coming years. Companies are increasingly looking for professionals who can develop and deploy machine learning models that can be used to solve real-world problems. As such, Machine Learning Engineers are expected to be in high demand in the near future.

[Education Path]
The recommended educational path for learners is to pursue a degree in Machine Learning. This degree typically requires a Bachelor's degree in Computer Science, Mathematics, or a related field. The degree program will cover topics such as supervised and unsupervised learning, probabilistic and non-probabilistic modeling, linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others. The degree program will also cover the development of algorithms for optimization and the application of these models to real-world situations.

The development trend for Machine Learning degrees is to focus on the application of Machine Learning models to real-world problems. This includes the development of algorithms for optimization and the application of these models to real-world situations. Additionally, the degree program will focus on the development of new models and methods for Machine Learning, such as deep learning and reinforcement learning.