Doctoral / PHD Programs in Artificial Intelligence and Machine Learning

4 universities offer graduate PHD program in Artificial Intelligence and Machine Learning

Harvard University logo
Ranked as:  #3 in Best National University
Tuition:  $50,654 per year
Total Cost:  $101,308 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  Massachusetts
Acceptance:  5.01%

Generative AI tools can reflect our failure of imagination and that is when the real learning starts.

Concerns abound academic integrity.

The cautious response is to be expected according to Houman Harouni, lecturer on education at the Harvard Graduate School of Education and a former elementary and high school teacher. He has compassion for educators trying to grapple with a rapidly shifting world shaped by machine learning.

Teacher education and professional development programs should not ignore generative artificial intelligence either.

Teach students to do what artificial intelligence cannot do.

Harvard EdCast: Educating in a World of Artificial Intelligence.

Alum helps young people understand how artificial intelligence is changing everything they know.

A doctoral student studies the benefits of immersive technology in the classroom.

Artificial intelligence.

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Embracing Artificial Intelligence in the Classroom - Harvard Graduate School of Education

  • GRE Required:  Yes
  • Research Assistantships:  864
  • Teaching Assistantships:  1388
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Columbia University in the City of New York logo
Ranked as:  #18 in Best National University
Tuition:  $51,194 per year
Total Cost:  $102,388 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  New York
Acceptance:  6.66%

New CBS research uncovers the surprising impact on performance, team coordination, and morale of integrating artificial intelligence into teams of human workers.

CBS PhD Student Lan Luo looks at AI interpretability.

Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence.

CBS PhD Student Lan Luo looks at AI interpretability CBS PhD Student Lan Luo looks at AI interpretability.

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Artificial Intelligence

  • GRE Required:  Yes
  • Research Assistantships:  1081
  • Teaching Assistantships:  1757
  • Financial Aid: Register to view the details
Carnegie Mellon University logo
Ranked as:  #22 in Best National University
Tuition:  $47,326 per year
Total Cost:  $94,652 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  Pennsylvania
Acceptance:  17.27%

The PhD in Statistics & Machine Learning program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. The Joint PhD Program in Machine Learning and Statistics is aimed at preparing students for academic careers in both CS and Statistics departments at top universities or industry.

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PhD in Statistics & Machine Learning

  • GRE Required:  Yes
  • Research Assistantships:  3032
  • Teaching Assistantships:  -
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71 universities offer the Master's program in Artificial Intelligence and Machine Learning.

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Tufts University logo
Ranked as:  #32 in Best National University
Tuition:  $55,168 per year
Total Cost:  $110,336 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  Massachusetts
Acceptance:  16.3%

Get the best results from ChatGPT by first understanding how it works and then learning from your conversations with it.

Start by understanding how it works and then learning from your interactions with it, says a human factors researcher.

Eight photos of elephants in daylight and nighttime with blue lines around individual elephants. Using artificial intelligence and drones, Tufts engineers and conservationists are helping track and protect wild elephants in East Africa.

Tufts Scientists Use Artificial Intelligence to Improve Tuberculosis Treatments.

School: School of Engineering Degree: Ph.D. in Electrical and Computer Engineering New job: Video Machine Learning Engineer at Apple, based in San Diego.

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Artificial Intelligence

  • GRE Required:  Register to view the details
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Ranked as:  #105 in Best National University
Tuition:  $50,442 per year
Total Cost:  $100,884 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  New York
Acceptance:  73.86%

Our research efforts aim to understand the nature of visual perception so that we can create visually intelligent machines. This is enabled via fundamental concepts in reasoning, prediction, supervised, semi-supervised and unsupervised learning, and stochastic optimization techniques. Our research involves (i) fundamental computer vision topics such as video analytics, detecting humans and their poses from images, object detection and tracking, etc (ii) computer vision applications such as medical image analysis, understanding social dynamics from videos of human interactions, collision detection in self-driving cars, and other vision-related regression problems in videos and (iii) the intersection of computer vision and graphics where we aim to model realistic avatars that interact naturally with humans. We are constantly pushing the boundaries in applying computer vision techniques to a myriad of problems such as 3D reconstruction of the heart from MRI images deception detection from visual cues, understanding group interactions such as in a volleyball game, improving STEM classroom learning through video analytics, and other such problems.

Our research spans the spectrum from theory to algorithms to applications. We are interested in developing novel computational methods for large-scale problems where conventional methods are often computationally infeasible. These methods include the development of novel estimators using tools from constrained optimization theory, convex analysis, and Bayesian nonparametrics. We are also interested in statistical problems involving covariance estimation for high-dimensional data, as lots of classical data analysis methods break down in high-dimensions. These methods have numerous applications in areas such as statistical genetics, image processing, computational biology, cognitive science, and natural language processing. On the theory side, we study questions motivated by the applications, and try to come up with new methods that are computationally feasible to fix the problems we have identified.

We focus on the development of novel computational problem solving methods based on abstractions of real-world processes. Specifically, the soft computing techniques we develop draw inspiration from how naturally occurring phenomena behave when adapting to various environmental situations. These techniques have applications in a wide range of fields such as physics, biology, and engineering. Our research in this area includes work in concurrent and distributed evolutionary algorithms, neuroevolution (automated design of neural networks), ant colony optimization, swarm intelligence, and neuro-cognitively motivated machine learning, which focuses on the design of learning algorithms and models strongly guided by principles in cognitive science and neuroscience.

Christopher M. Homan: natural language and speech processing, multiagent systems, machine learning.

Xumin Liu: machine learning, natural language and speech processing.

Alexander Ororbi nature-inspired and evolutionary computation, machine learning.

This course will cover the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian, maximizing a posteriori (MAP), and maximum likelihood (ML) parameter estimation, regularization and sparsity-promoting priors, kernel methods, adaptive basis function methods, the expectation maximization algorithm, Monte Carlo methods, variational methods, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples. Each student will complete several problem sets, including both mathematical and computer implementation problems. Probability and Statistics I, Linear Algebra, and Introduction to Computer Programming. Familiarity with a numerical mathematics package (e.g. Matlab, Maple, Mathematica) is helpful but not required.

Deep learning represents a set of emerging techniques in machine learning that has quickly become prevalent in the analysis of big data. The power and potential of this recent breakthrough in intelligent computer systems has been demonstrated through many successes. Deep learning systems are the current best performer in computer vision and speech processing. A wide variety of active researches are being conducted to leverage the capability of deep learning for achieving automation in areas such as autonomous driving, robotics, and automated medical diagnosis. There is a crucial need to educate our students on such new tools. This course gives an in-depth coverage of the advanced theories and methods in deep learning including basic feedforward neural networks, convolutional neural networks, recurrent neural networks including long short-term memory models, deep belief nets, and autoencoders. It will make an emphasis on approaches with practical relevance, and discusses a number of recent applications of deep networks applications in computer vision, natural language processing and reinforcement learning.

This course provides an introduction in the fundamentals of working with quantitative information. Topics include matrix algebra (matrices, vectors, direct and indirect methods for solving linear systems, eigenvectors, singular value decomposition, least-squares systems) optimization (convex analysis, gradient descent, Newton method, interior-point methods), statistics (random variables, p-values, hypothesis testing, confidence intervals) and data exploration (clustering, dimensionality reduction, curve fitting).

Everyone uses modeling and simulation even without being aware of it. This course talks mathematical and computational modeling and simulation as the tools to solve complex problems in the real world. Topics are divided by the category of modeling method: phenomenological models vs. mechanistic models. For mechanistic models, the course will cover differential equations (including variational principle to construct the differential equations, solutions to ordinary differential equations (ODE), and classical ODE systems) and cellular automaton in detail, and mention other mechanistic models. Similarly, for phenomenological models, the course will cover regression and neural networks in detail, and introduce other phenomenological models such as networks and power-law distributions. In parallel, paper review and discussion will serve as case studies of modeling of real-world complex systems, illustrating application domains. Course projects are required.

An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral written summaries of research papers are required.

There have been significant advances in recent years in the areas of neuroscience, cognitive science and physiology related to how humans process information. In this course students will focus on developing computational models that are biologically inspired to solve complex problems. A research paper and programming project on a relevant topic will be required. A background in biology is not required.

The course will introduce students to the application of intelligent methodologies applications in computer security and information assurance system design. It will review different application areas such as intrusion detection and monitoring systems, access control and biological authentication, firewall structure and design. The students will be required to implement a course project on design of a particular security tool with an application of an artificial intelligence methodology and to undertake research and analysis of artificial intelligence applications in computer security.

This course examines current topics in Intelligent Systems. Specific course instances will be identified as belonging to the Intelligent Systems cluster, the Computational Vision and Acoustics cluster, the Security cluster, or some combination of these three clusters. Course offered every other year.

Machine intelligence teaches devices learn a task without explicitly programming them do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning).

This course offers an opportunity to learn a specific seminar topic in depth.

This course provides students with exposure to foundational data analytics technologies, focusing on unstructured data. Topics include unstructured data modeling, indexing, retrieval, text classification, text clustering, and information visualization.

Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains.

Dynamic Resource Allocation [Hosseini]: Through the integration of artificial intelligence (AI), economics, and computation this project investigates novel solutions for resource allocation in dynamic environments and situations that lack transferable currency. With the advent of online platforms, economic theory emerges as a fundamental approach to promote desirable social properties of efficiency, fairness, and truthfulness in a variety of domains such as shift scheduling, course registration, cloud computing, and crowdsourcing. This project tackles a variety of fundamental problems at the intersection AI and economics while enriching the algorithmic and societal understanding of resource allocation in dynamic settings. This contrasts with classical mechanisms that either focus solely on economic aspects of resource allocation in static and offline settings or disregarded social aspects such as fairness. Specifically, the project studies two interconnected components: (1) sequential allocation under uncertainty, by synthesizing models studied in AI with economic theory to investigate, analyze, and create new mechanisms that are fair and discourage strategic manipulation in environments where agents' preferences are evolving (e.g. nurse scheduling and course allocation) and (2) online mechanisms, by employing insights from algorithm design and AI to study fairness and efficiency of allocation mechanisms when agents arrive and depart over time or the availability of items is uncertain (e.g. food bank organizations and crowdsourcing platforms).

Multiagent Bug Assignment [Hosseini]: Bug assignment in large software projects is typically a time-consuming and tedious task effective assignment requires that bug triagers hold significant contextual information both the reported bugs and the pool of available developers. expertise by learning their traits, and 3) utilizing techniques from multi-agent systems to efficiently and fairly assign bug reports to relevant developers. We will use data from various bug repositories, such as Eclipse and Firefox, to train our model and evaluate its efficiency against the current state-of-the-art approaches that rely solely on machine learning techniques.

Label Distribution Learning [Homan]: Machine learning models learn from human annotated labeled data. The annotation is often subjective and based on their personal experiences. In supervised learning, the multiple annotations per data item is usually reduced to a single label representing the ground truth. This hides the diversity and objectivity of the labels. Label distribution learning associates a probability distribution for each data item preserving the diversity between labels.

Neurocognitively-Inspired Lifelong Machine Learning [Ororbia]: Neural architectures trained with back-propagation of errors are susceptible to catastrophic forgetting. In other words, old information acquired by these models is lost when new information for new tasks is acquired. This makes building models that continually learn extremely difficult if not near impossible. The focus of our research is to draw inspiration from models of cognition and biological neurocircuitry, as well as theories of mind and brain functionality, to construct new learning procedures and architectures that generalize across tasks and continually adapt to novel situations, combining input from multiple modalities sensory channels.

Video-Based Search for ASL Dictionaries [Huenerfauth]: Looking up an unfamiliar word in a dictionary is a common activity in childhood or foreign-language education, yet there is no easy method for doing this in ASL. We are investigating a computer-vision-based sign-lookup interface for online ASL video dictionaries.

ASL Corpora for Linguistic Research [Huenerfauth]: We collect video and motion-capture recordings of native sign-language users, in support of linguistic research and machine-learning modeling of aspects of ASL.

Learning ASL through Real-Time Practice [Huenerfauth]: We are investigating how computer-vision technologies can enable students learning American Sign Language (ASL) to practice their signing independently, through a tool that provides feedback automatically based on a video of their signing.

Generating ASL Animation from Motion-Capture Data [Huenerfauth]: We investigate techniques for making use of motion-capture data collected from native American Sign Language (ASL) signers to produce linguistically accurate animations of ASL. We produce machine-learning models of various phenomena, e.g. speed and timing during signing, and we use these models to partially automate the generation of animations, to reduce the cost in providing ASL content for Deaf users.

Visual Prediction using Multimodal Data [Kong]: This project develops deep learning-based methods for predicting future human actions and visual frames from large-scale video data. Thanks to the extra data including audio and text data, we can create knowledge base that provides us with rich prior information, and help achieve accurate and reliable visual prediction.

Data Driven Adaptive and Robust Subspace Learning with Computer Vision Applications [Markopoulos]: We design Data Driven Adaptive Learning (DDAL) frameworks for robust subspace tracking, combined with deep learning architectures. Motivating applications that we explore include people detection, object detection, and change detection in new domains that are markedly different from those used for training. Data starved environments are also of interest.

Signal Processing, Data Analysis, and Machine Learning for Indoors Radar-based Motion Recognition Applications in Assisted Living [Markopoulos]: In this project, we apply adaptive machine learning methods for motion recognition based on micro-Doppler signatures, collected from indoors radar measurements. A key application is real-time fall detection towards safer self-dependent living and aging-in-place.

Gait Recognition from Wearable Sensors with Application in Injury Prediction and Athlete Rehabilitation [Markopoulos]: This project focuses on gait motion classification based on acceleration signals collected from low-cost commercial wearable inertia measurement units. The project tasks span from dataset collection to multi-way data analysis and machine learning. Our final goal is to deliver adaptive and transferable machine learning for fatigue estimation and real-time injury prediction.

Role of Emotional Regulation in the Workplace [Nwogu]: Emotional regulation refers to refers to the ability to respond to a range of emotions in a manner that is controllable and socially tolerable. To this end, we designed an experiment where participants experienced a range of emotions and were required to respond in pre-specified manners. Their neurological, physiological and expressive manifestations of emotion were recorded and now we are using deep learning and other statistical techniques techniques to better understand how changes in sympathetic activations are exhibited across modalities.

ASR for Resource Constrained Languages [Ptucha]: Developing Automatic Speech Recognition with little training data. Developing unique generative models with multiple transfer learning on acoustic models along with statistical based language models.

Graph CNN [Ptucha]: While CNNs have transformed the machine learning landscape, they do not work with generic graphs such as those describing protein structures, social media graphs, or point clouds. This research is discovering new convolution and pooling methods which work generically on heterogeneous graphs.

Learning Disentangled Representations [Linwei]: We develop deep representation learning methods that are able to separate these inter-subject variations from clinical data. We work with clinicians to deploy such deep-learning based software tools to guide clinicians progressively closer towards the surgical target in real time during the procedure.

End-to-End Uncertainty Quantification [Linwei]: Mathematical models of a living system are always subject to epistemic uncertainties that represent our limited knowledge a system. While personalized models have shown increasing potential in medicine, their uncertainties remain the main roadblock to their widespread adoption in the healthcare industry. We develop novel active learning based approaches to first infer the uncertainty within the data-driven model elements, before propagating this uncertainty to model predictions.

Transferring Simulation Data to Real Data [Linwei]: A primary factor for the success of machine learning is the quality of labeled training data. However, in many fields, labeled data can be costly, difficult, or even impossible to acquire. In comparison, computer simulation data can now be generated at a much higher abundance with a much lower cost. We develop machine learning and deep learning techniques that are able to leverage the knowledge in simulation data and transfer it to real data based tasks.

Intelligent Security Systems [Reznik]: The project designs a curriculum, develops course materials, tests and evaluates them in real college classroom settings, prepares and submits them for dissemination of a college level course on Intelligent Security Systems. In order to facilitate interconnections with other courses and its inclusion into the national Cybersecurity curricula, the course is composed of nine separate modules. Five modules cover the specialized topics including: a review of the modern state of the cybersecurity and the current problems and approaches firewall design intrusion detection systems anti-malware methods and tools hacking activity and attack recognition and prevention. Other modules provide additional support to assist in course teaching preparation, such as test and exam questions, course project and research assignment specifications, and tool presentation descriptions. This course idea is innovative and unique. It merges together various knowledge areas as diverse as artificial intelligence and machine learning techniques with computer security systems and applications. The course will allow to instill into students a unique knowledge in the very intense domain and will lead students towards getting much better prepared to their practical work ahead. It combines theoretical knowledge and practical skills development. Also, it advances students research, communication and presentation skills.

Projects in the REU in Computational Sensing for Human-aware AI [Alm, Bailey, Geigel, Huenerfauth, Ptucha, Shinohara]: The REU Site in Computational Sensing for Human-centered Artificial Intelligence recognizes that as the boundaries between HCI and AI blur, and AI grows increasingly agile and pervasive, the next generation of computational scientists must be capable of responsibly and effectively leveraging a spectrum of sensing data from data-generating humans. With this focus, the REU Site will expand its trajectory as an attractor for highly diverse students who will gain experience with sensing hardware and software towards transformative advances in intelligent systems focused on human behaviors and cognitive processes. Enabling diverse stakeholders early in their careers to discover collect, fuse, make inference with, and visualize multimodal human data can transform how humans and machines engage and collaborate. The research in th

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Artificial Intelligence

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Oregon State University logo
Ranked as:  -
Tuition:  $28,121 per year
Total Cost:  $56,242 * This tuition data is based on IPEDS. For the latest tuition amount, refer to the respective college websites.
State:  Oregon
Acceptance:  82.21%

Oregon State University's Ph.D. and MS degree programs in Artificial Intelligence (AI) has a long history of excellence in AI since the early days of computer science. The field traces its origin to many disciplines, including philosophy, psychology, mathematics, and engineering. Today, AI is making contributions to all areas of intellectual and artistic endeavor. To encompass this diversity, the new AI program creates a direct pathway for motivated and capable students from any discipline to enter the field of AI and make new research contributions.

In addition to offering many courses and supporting research in the core topics of AI such as machine learning, knowledge representation, reasoning under uncertainty, sequential decision making, natural language processing, and computer vision and robotics, the program allows great flexibility for students to choose relevant courses from a wide range of other disciplines.

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PH.D. and M.S. in Artificial Intelligence

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What kind of scholarships are available for Graduate Programs in Artificial Intelligence and Machine Learning?

We have 209 scholarships awarding up to $2,025,183 for Masters program in for Artificial Intelligence and Machine Learning, targeting diverse candidates and not restricted to state or school-based programs.

Scholarship nameAmountCredibility
ASNT Fellowship Award$20,000High
Google Lime Scholarship$5,000High
The Innovator’s Grant$3,600High
GeneTex Scholarship Program$2,000High
GMiS STEM Scholarships$500High

Find scholarships and financial aid for Artificial Intelligence and Machine Learning graduate programs

$500 $20000

How can I compare the Artificial Intelligence and Machine Learning Graduate Programs?

Compare the GRE score requirements, admission details, credit requirements and tuition for the Master's Program, from 71 universities offering Graduate PHD/Doctoral Programs in Artificial Intelligence and Machine Learning. Compare Graduate PHD/Doctoral Programs in Artificial Intelligence and Machine Learning

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