Machine Learning has emerged as an important area of research and practice influencing many diverse application domains and industries inclusive of all engineering disciplines. This concentration builds on common required coursework in the undergraduate engineering curriculum and offers a pathway for undergraduate engineering students to focus their studies in this area and demonstrate to future employers and graduate school admission committees that they have the necessary background. The goals of this concentration are to equip students with skills to select, adapt, optimize, or design machine learning algorithms for various applications, assess their performance, gain experience utilizing related software or hardware tools, curate, visualize, or analyze data of various types, and read and explain methods from the machine learning literature.<\/p>\r\n\r\n\r\n\r\n
The 12-unit concentration is available to students in any of the College of Engineering\u2019s bachelor\u2019s degree programs. The concentration is noted on students\u2019 official transcripts and will prepare students to work in the areas of Machine Learning applied to technological systems used for product design and function, as well as domain-specific data interpretation.<\/p>\r\n\r\n\r\n\r\n
It is recommended that students interested in pursuing a concentration in Machine Learning\u00a0declare<\/a> their concentration as early as possible in their degree program in order to facilitate course planning, but in no case later than May 1 of a student\u2019s junior year. Course requirements are found below,\u00a0in addition to details regarding the required experience component.<\/p>\r\n\r\n\r\n\r\n Required Courses<\/strong> (Choose one of the following.)<\/p>\r\n\r\n\r\n\r\n ENG EC 414 \u2013 Introduction to Machine Learning<\/a> Additional Courses<\/strong> (Choose any two electives from any list below. The two courses need not be from the same list; the lists are for guidance purposes only and highlight thematic groupings.)<\/p>\r\n\r\n\r\n\r\n Models, Learning, and Inference<\/strong><\/p>\r\n\r\n\r\n\r\n ENG EC 418 \u2013 (Olshevsky) Reinforcement Learning<\/a> Optimization, Algorithms, and Programming<\/strong><\/p>\r\n\r\n\r\n\r\n ENG EC 525 \u2013 Optimization for Machine Learning<\/a> Applications<\/strong><\/p>\r\n\r\n\r\n\r\n ENG BE 403 \u2013 Biomedical Signals and Controls<\/a> (cannot be applied for BME<\/b> majors) Please note<\/strong> that courses required (by name\/number) for the student\u2019s major cannot also be counted towards the three courses applied to fulfill the concentration. Specifically, EC401<\/b> cannot apply for Electrical Engineering majors and BE403<\/b> cannot apply for Biomedical Engineering Majors.<\/p>\r\n \u2020Note: the relevant BE\/EC 500 courses above marked with this symbol were taught in the following semesters:<\/strong><\/p>\r\n\r\n\r\n\r\n 1<\/sup>\u00a0<\/em>Fall 2019, Fall 2021, Fall 2022 2. Experiential Component Requirement<\/strong><\/p>\r\n\r\n\r\n\r\n Completion of a well-defined experiential component, which must be clearly related to Machine Learning, is required. Options for experiences include a senior design project, laboratory research, internships, directed study, experiential courses, and others. Term projects in courses may satisfy the experiential requirement if they have sufficient Machine Learning content.\u00a0Prior to proposing an experiential component, the student must have completed a required course.<\/strong><\/p>\r\n This requirement must be approved by the Concentration Coordinator<\/a> and the Experiential Component Approval, along with a written proposal, form<\/a>\u00a0must be submitted to the Undergraduate Records Office<\/a> prior to beginning the component. Upon conclusion, an Experiential Component Summary form<\/a> including a written summary of the experiential component must also be submitted for approval by 5pm on the due date.<\/em><\/strong>\r\n\r\n<\/p>\r\n \r\n\r\n<\/p>\r\n Detailed Requirements and Instructions<\/a> \r\n\r\n<\/p>\r\n \r\n\r\n<\/p>\r\n Undergraduate Programs & Records Office: 617-353-6447 or\u00a0engrec@bu.edu<\/a> <\/p>","protected":false},"excerpt":{"rendered":" Machine Learning is an important area of research and practice influencing many diverse application domains and industries. Students may add this concentration to any undergraduate degree within the College of Engineering.<\/p>\n","protected":false},"author":8588,"featured_media":122122,"parent":0,"menu_order":5,"template":"","meta":{"_editor_template":""},"bu_progs_page-type":[4],"bu_progs_degree-option":[102],"bu_progs_education-level":[212],"bu_progs_area-of-study":[173,198,176],"bu_progs_format":[111],"bu_progs_availability":[],"bu_progs_location":[117],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu-program-page\/120863"}],"collection":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu-program-page"}],"about":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/types\/bu-program-page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/users\/8588"}],"version-history":[{"count":50,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu-program-page\/120863\/revisions"}],"predecessor-version":[{"id":172314,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu-program-page\/120863\/revisions\/172314"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/media\/122122"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/media?parent=120863"}],"wp:term":[{"taxonomy":"bu_progs_page-type","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_page-type?post=120863"},{"taxonomy":"bu_progs_degree-option","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_degree-option?post=120863"},{"taxonomy":"bu_progs_education-level","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_education-level?post=120863"},{"taxonomy":"bu_progs_area-of-study","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_area-of-study?post=120863"},{"taxonomy":"bu_progs_format","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_format?post=120863"},{"taxonomy":"bu_progs_availability","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_availability?post=120863"},{"taxonomy":"bu_progs_location","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/bu_progs_location?post=120863"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Concentration Requirements<\/strong><\/h2>\r\n\r\n\r\n\r\n
\r\n
ENG EC 503 \u2013 Learning from Data<\/a><\/p>\r\n\r\n\r\n\r\n
ENG EC 505 \u2013 Stochastic Processes and Inference<\/a>
ENG EC 517 \u2013 Introduction to Information Theory<\/a>
ENG EC 523\/ CAS CS 523 \u2013 Deep Learning<\/a><\/p>\r\n\r\n\r\n\r\n
ENG EC 504 \u2013 Advanced Data Structures and Algorithms<\/a>
ENG EC\/SE 524 \u2013 Optimization Theory and Methods<\/a>
ENG EC 526 \u2013 Parallel Programming for High Performance Computing<\/a>
ENG EC 527 \u2013 High Performance Programming with Multicore and GPUs<\/a>
ENG EC 528 \u2013 Cloud Computing<\/a><\/p>\r\n\r\n\r\n\r\n
ENG BE 500\u20203<\/sup><\/em>\u00a0\u2013 AI and Systems Biology
ENG BE 500\u20206<\/sup> – Deep Learning for Biomedical Images and Signals
ENG BE\/EC 519 Speech Processing by Humans and Machines<\/a>
ENG BE 562 \u2013 Computational Biology: Machine Learning Fundamentals<\/a>
ENG BE 570 \u2013 Introduction to Computational Vision<\/a>
ENG ME 404 – Dynamics and Control of Mechanical Systems<\/a>
ENG ME 416 \u2013 Introduction to Robotics<\/a>
ENG ME 570 \u2013 Robot Motion Planning<\/a>
ENG EC 401 \u2013 Signals and Systems<\/a> (cannot be applied for EE<\/b> majors)
ENG EC 402 – Control Systems<\/a>
ENG EC 415 \u2013 Software Radios<\/a>
ENG EC 516 – Digital Signal Processing<\/a>
ENG EC 518 – Robot Learning<\/a>
ENG EC 500\u20205<\/sup><\/em> \u2013 (Ohn-Bar) Robot Learning and Vision for Navigation
ENG EC 520 \u2013 Digital Image Processing and Communication<\/a><\/p>\r\n\r\n\r\n\r\n
2\u00a0<\/sup>Spring 2021, Spring 2022
3<\/sup><\/em>\u00a0Spring 2020
4<\/sup><\/em>\u00a0Fall 2020, Fall 2022 (EC 418)
5<\/sup><\/em> Fall 2023
6 <\/sup><\/em>Spring 2024<\/p>\r\n\r\n\r\n\r\nResources<\/h2>\r\n
Declaration<\/a>
Experiential Component Proposal<\/a> <\/a>
Experiential Component Summary<\/a>
SARA Complaint Process<\/a><\/p>\r\nContact<\/h2>\r\n
Concentration Coordinator: Prof. Hamid Nawab (hamid@bu.edu)<\/a><\/p>\r\n