This online, part-time master's program accelerates careers in industry or research by combining rigorous mathematical foundations with practical skills in machine learning and data science. Students will develop expertise in handling complex datasets, implementing scalable solutions with industry-standard tools such as PySpark. The curriculum enhances analytical capabilities in mathematics and statistics, explores the limitations of machine learning techniques, and emphasizes ethical application. Topics include statistical estimation, prediction, anomaly detection, probability, decision theory, advanced deep learning, reinforcement learning, supervised and unsupervised learning, Bayesian methods, and unstructured data processing. Practical application of knowledge is facilitated through project work and real-world case studies.
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This online, part-time master's program accelerates careers in industry or research by combining rigorous mathematical foundations with practical skills in machine learning and data science. Students will develop expertise in handling complex datasets, implementing scalable solutions with industry-standard tools such as PySpark. The curriculum enhances analytical capabilities in mathematics and statistics, explores the limitations of machine learning techniques, and emphasizes ethical application. Topics include statistical estimation, prediction, anomaly detection, probability, decision theory, advanced deep learning, reinforcement learning, supervised and unsupervised learning, Bayesian methods, and unstructured data processing. Practical application of knowledge is facilitated through project work and real-world case studies.
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400 Students
AI/ML, Systems, Theory, Human-Computer Interaction
Access to cutting-edge research labs and industry partnerships
No Admission Requirement Available.
No Application Start time & Deadline Available
September 2026
["Individual Project", "Core modules", "You\u2019ll take all of these core modules", "Core modules", "Programming for Data Science", "Gain fluency in both R and Python, for proficient use in later modules. Topics include random number generation, using vectors and matrices, working with APIs, and reading from/writing to different file formats.", "The module also covers practices for ensuring code correctness, such as profiling, debugging and unit testing, and how to package code for distribution.", "Applicable Mathematics", "Gain familiarity with statistical and mathematical tools that will be used in later modules.", "You\u2019ll review the fundamentals of calculus, linear algebra, and probability theory, as well as other topics including matrix decomposition techniques, convergence of random variables, sample-based statistical inference, and numerical optimisation methods."]
No academic requirements found.
No English language requirements found.
Applicants must meet specified entry criteria, including relevant academic qualifications and professional experience. Submission of a personal statement and application reference is required. Additional requirements may include teacher references, adherence to contextual admissions policies, and performance on relevant admissions tests such as the Engineering and Science Admissions Test (ESAT), Test of Mathematics for University Admissions (TMUA), or the University Clinical Aptitude Test (UCAT). Deadlines vary by application cycle.
₹47,62,863/Year
Annual Fee
24 months
Total Duration
No Living costs available.
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Amount
Various benefits
Deadline
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21000 GBP
Deadline
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Amount
1250 USD
Deadline
Not specified
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Amount
Various benefits
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Amount
Various benefits
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Amount
2000 USD
Deadline
15 Apr 2025
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Amount
300 EUR
Deadline
Anytime
No Eligibility Requirements Available
No Application Steps Available
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