Online Machine Learning Master's Program

This course will give students a rigorous introduction to the foundations of machine learning, including but not limited to frequently used tools in linear algebra, calculus, probability, and widely applied methods such as linear regression and support vector machines. In addition, this course provides hands-on training on implementing these algorithms via python from scratch. Students will be trained to use poplar python libraries such as numpy, scipy and matplotlib. Not for CS undergrad majors.

An elective course from the list of ELECTIVE COURSES

Natural language processing (NLP) is one of the most important technologies in the era of information. Comprehending human language is also a crucial and challenging part of artificial intelligence. People communicate almost everything in language: conferences, emails, customer service, language translation, web searches, reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have achieved high performance in many different NLP tasks. Instead of traditional and task-specific feature engineering, deep learning can solve tasks with single end-to-end models. The course provides an introduction to machine learning research applied to NLP. We will cover topics including word vector representations, neural networks, recurrent neural networks, convolutional neural networks, semi-supervised models, reinforcement learning for NLP, as well as some attention-based models.

Prerequisite: Undergrad linear algebra and probability OR CS 556

In this course we will talk about the foundational principles that drive machine learning applications and practice implementing machine learning algorithms. Specific topics include supervised learning, unsupervised learning, neural networks, and graphical models. The main goal of the course is to equip you with the tools to tackle new ML problems you might encounter in life.

Prerequisite: Undergrad linear algebra and probability OR CS 556

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real world applications such as face recognition, machine translation, self-driving car, recommender system, playing the Go game, etc. This course is designed for students either with or without ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended by not required. Knowledge in machine learning and artificial intelligence is helpful but unnecessary. 

Prerequisite: Undergrad linear algebra and probability OR CS 556

Machine learning aims to extract useful information from the data, and to build an accurate model on top of the extracted information for future prediction. There are two important aspects that have to be taken into account for a machine learning problem: how can we develop computationally efficient algorithms to learn useful information, and what is the prediction performance of the algorithm on unseen data. More importantly, is it possible to achieve the best of the two worlds, or there has to be some trade-off. This course will introduce students to concepts relating the computational efficiency and the statistical accuracy for a broad range of problems, including regression, classification, clustering, adaptive learning, to name a few. It will cover popular numerical methods that carry out state-of-the-art performance on the computational side, and it will also discuss possible improvement in the price of estimation accuracy and memory usage. The goal of the course is to help students understand these trade-offs from a theoretical perspective and guide them to design near-optimal algorithms for real-world problems.

Prerequisite: CS 559 Machine Learning: Fundamentals and Applications

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Colloquially, the term "artificial intelligence" is used to describe machines that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. The course will emphasize on both learning and problem solving, and will develop rigorous statistical models for real-world AI applications. The course will also deliver modern optimization techniques to find an optimal model for a given problem. It will require a math background in calculus, linear algebra and probability, and programming skills in Python or Matlab.

Prerequisite: Undergrad linear algebra and probability OR CS 556