Machine Learning PhD Programs

Attaining a PhD in Machine Learning requires you to take courses to implement predictive modeling, formulate optimization problems, and represent big data sets. Read on to learn more about this program's common courses and admission requirements.

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Machine learning can be studied as either an independent field or a specialization of computer information science. Students will find the coursework is often very heavy in mathematics. Application to these programs will sometimes require that students have previous experience with relevant coursework.

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Courses in Machine Learning

Machine learning courses cover a variety of topics that deal with learning about large data sets and how to apply statistical methods. Students can anticipate taking approximately 43 hours of coursework prior to graduation, not including time spent on a dissertation.

Introduction to Machine Learning

Initial coursework in machine learning introduces students to the design of systems so they learn from the data. Students are supervised and learn more about predictive modeling, Bayesian methods, and neural networks. The goal of this introductory course is to demonstrate to students the processes used in this field and the ways that computer programs can improve the performance of machines.

Convex Optimization

Coursework in this area introduces students to the fundamentals of optimization, including mathematical fundamentals, modeling, and algorithms. The course introduces students to the optimization of functions as a means of answering problems that arise in machine learning. Students learn about KL divergence and exponential modeling and classification and clustering. Prior to the course, students should be knowledgeable about programming languages.

Big Data / Large Data Sets

Big data in the context of machine learning revolves around analyzing very large datasets. Students learn more about the difficulties involved with working with large datasets, including the difficulties in visualizing them and understanding errors that arise in them. Means of representing large data are taught, enabling students to learn about graphical models, clustering, and latent factor models.

Statistical Machine Learning

Statistical learning topics in machine learning cover statistical learning, graphical probability models, and how areas like convex optimization relate to statistics. Student learn more about statistical inference and how to apply statistics in classification, prediction, regression, clustering, and data exploration. Coursework in statistical machine learning deals not only with the theory but the applications of statistics in this field.

Pattern Recognition

Pattern recognition involves studying how patterns in machine learning are modeled. Students learn more about factor graphs, standard models as graphical models, and Bayesian networks. Incoming students should understand linear algebra, probabilities, and other mathematical aspects in the area of computer science.

Applying to the Program

Applicants trying to get into a machine learning program will have to submit letters of recommendation vouching for why they should be admitted. They will also need to submit GRE scores, though students whose second language is English will also need to submit their TOEFL scores. Prior academic performance will be reviewed before an applicant is accepted.

Time spent studying machine learning will include lessons about large data sets, pattern recognition, and statistical application. The entire program may require no more than 2.5 years of full-time enrollment to complete, during which students will become more proficient in analyzing technical problems and representing data in a variety of ways.

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