Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Introduction to Computer Science I-II. Prerequisite(s): CMSC 15400. Note(s): A more detailed course description should be available later. STAT 37750: Compressed Sensing (Foygel-Barber) Spring. More than half of the requirements for the minor must be met by registering for courses bearing University of Chicago course numbers. CMSC27230. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. In recent offerings, students have written a course search engine and a system to do speaker identification. STAT 34000: Gaussian Processes (Stein) Spring. Students may petition to take more advanced courses to fulfill this requirement. Topics include: algebraic datatypes, an elegant language for describing and manipulating domain-specific data; higher-order functions and type polymorphism, expressive mechanisms for abstracting programs; and a core set of type classes, with strong connections to category theory, that serve as a foundational and practical basis for mixing pure functions with stateful and interactive computations. Basic counting is a recurring theme and provides the most important source for sequences, which is another recurring theme. STAT 37500: Pattern Recognition (Amit) Spring. Reading and Research in Computer Science. CMSC14300. Features and models Part 1 covered by Mathematics for. Students who earn the BA are prepared either for graduate study in computer science or a career in industry. This course will introduce fundamental concepts in natural language processing (NLP). Random forests, bagging 100 Units. The course will involve a business plan, case-studies, and supplemental reading to provide students with significant insights into the resolve required to take an idea to market. Terms Offered: Winter Errata ( printing 1 ). 100 Units. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Lecture 1: Intro -- Mathematical Foundations of Machine Learning Prerequisite(s): CMSC 15400 Cambridge University Press, 2020. https://canvas.uchicago.edu/courses/35640/, https://edstem.org/quickstart/ed-discussion.pdf, The Elements of Statistical Learning (second edition). 100 Units. 100 Units. Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. Other new courses in development will cover misinterpretation of data, the economic value of data and the mathematical foundations of machine learning and data science. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. Appropriate for graduate students or advanced undergraduates. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. 100 Units. Note(s): Students who have taken CMSC 11800, STAT 11800, CMSC 12100, CMSC 15100, or CMSC 16100 are not allowed to register for CMSC 11111. Introduction to Computer Science II. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Lecure 2: Vectors and matrices in machine learning notes, video, Lecture 3: Least squares and geometry notes, video, Lecture 4: Least squares and optimization notes, video, Lecture 5: Subspaces, bases, and projections notes, video, Lecture 6: Finding orthogonal bases notes, video, Lecture 7: Introduction to the Singular Value Decomposition notes video, Lecture 8: The Singular Value Decomposition notes video, Lecture 9: The SVD in Machine Learning notes video, Lecture 10: More on the SVD in Machine Learning (including matrix completion) notes video, Lecture 11: PageRank and Ridge Regression notes video, Lecture 12: Kernel Ridge Regression notes video, Lecture 13: Support Vector Machines notes video, Lecture 14: Basic Convex Optimization notes video, Lectures 15-16: Stochastic gradient descent and neural networks video 1, video 2, Lecture 17: Clustering and K-means notes video, This term we will be using Piazza for class discussion. CMSC27530. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. There is one approved general program for both the BA and BS degrees, comprised of introductory courses, a sequence in Theory, and a sequence in Programming Languages and Systems, followed by advanced electives. Instead of following an explicitly provided set of instructions, computers can now learn from data and subsequently make predictions. Placement into MATH 15100 or completion of MATH 13100. CMSC10450. Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. CMSC22200. We teach the "Unix way" of breaking a complex computational problem into smaller pieces, most or all of which can be solved using pre-existing, well-debugged, and documented components, and then composed in a variety of ways. Logistic regression Advanced Algorithms. Equivalent Course(s): MATH 27700. Terms Offered: Winter Data science provides tools for gaining insight into specific problems using data, through computation, statistics and visualization. CMSC11900. Existing methods for analyzing genomes, sequences and protein structures will be explored, as well related computing infrastructure. The new paradigm of computing, harnessing quantum physics. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. 100 Units. 100 Units. Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Prerequisite(s): CMSC 15400. No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. Although this course is designed to be at the level of mathematical sciences courses in the Core, with little background required, we expect the students to develop computational skills that will allow them to analyze data. Appropriate for graduate students oradvanced undergraduates. CMSC 29700. This story was first published by the Department of Computer Science. Students are expected to have taken calculus and have exposure to numerical computing (e.g. CMSC22010. We will introduce the machine learning methods as we go, but previous familiarity with machine learning will be helpful. We'll explore creating a story, pitching the idea, raising money, hiring, marketing, selling, and more. Click the Bookmarks tab when you're watching a session; 2. Instructor(s): A. ChienTerms Offered: Winter Networks help explain phenomena in such technological, social, and biological domains as the spread of opinions, knowledge, and infectious diseases. Equivalent Course(s): CMSC 33250. By using this site, you agree to its use of cookies. Students will complete weekly problem sets, as well as conduct novel research in a group capstone project. Introduction to Human-Computer Interaction. CMSC21400. CMSC22900. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Neural networks and backpropagation, Density estimation and maximum likelihood estimation The course will be fast moving and will involve weekly program assignments. CMSC22880. (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries. Introduction to Computer Science I. I'm confident the University of Chicago data science major, with the innovative clinic model, will produce well-rounded graduates who will thrive in any industry. Instructor(s): S. KurtzTerms Offered: Spring Thanks to the fantastic effort of many talented developers, these are easy to use and require only a superficial familiarity . The class provides a range of basic engineering techniques to allow students to develop their own actuated user interface systems, including 3D mechanical design, digital fabrication (e.g. Basic processes of numerical computation are examined from both an experimental and theoretical point of view. Numerical Methods. This exam will be offered in the summer prior to matriculation. Honors Combinatorics. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. Programming Languages and Systems Sequence (two courses required): Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam must replace it with an additional course from this list, Understanding . Prerequisite(s): CMSC 15400 or CMSC 22000. Programming projects will be in C and C++. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. The course discusses both the empirical aspects of software engineering and the underlying theory. This site uses cookies from Google to deliver its services and to analyze traffic. Topics include program design, control and data abstraction, recursion and induction, higher-order programming, types and polymorphism, time and space analysis, memory management, and data structures including lists, trees, and graphs. CMSC15400. Reading and Research in Computer Science. 100 Units. 100 Units. A core theme of the course is "scale," and we will discuss the theory and the practice of programming with large external datasets that cannot fit in main memory on a single machine. Standard machine learning (ML) approaches often assume that the training and test data follow similar distributions, without taking into account the possibility of adversaries manipulating either distribution or natural distribution shifts. We will build and explore a range of models in areas such as infectious disease and drug resistance, cancer diagnosis and treatment, drug design, genomics analysis, patient outcome prediction, medical records interpretation and medical imaging. Graduate and undergraduate students will be expected to perform at the graduate level and will be evaluated equally. The textbooks will be supplemented with additional notes and readings. Plan accordingly. | Learn more about Rohan Kumar's work experience, education . Announcements: We use Canvas as a centralized resource management platform. (And how do we ensure this in the presence of failures?) The UChicago/Argonne team is well suited to shoulder the multidisciplinary breadth of the project, which spans from mathematical foundations to cutting edge data and computer science concepts in artificial . In recent offerings, students have written programs to simulate a model of housing segregation, determine the number of machines needed at a polling place, and analyze tweets from presidential debates. Learning goals and course objectives. These courses may be courses taken for the major or as electives. 100 Units. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. A-: 90% or higher STAT 37601/CMSC 25025: Machine Learning and Large Scale Data Analysis (Lafferty) Spring. These scientific "miracles" are robust, and provide a valuable longer-term understanding of computer capabilities, performance, and limits to the wealth of computer scientists practicing data science, software development, or machine learning. Introduction to Computer Vision. A major goal of this course is to enable students to formalize and evaluate theoretical claims. Students will also gain basic facility with the Linux command-line and version control. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. Roger Lee : Mathematical Foundations of Option Pricing/Numerical methods . Instructor(s): K. Mulmuley For this research, they studied the chorismate mutase family of metabolic enzymes, a type of protein that is important for life in many bacteria, fungi, and plants. There are roughly weekly homework assignments (about 8 total). Programming Languages. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. This course focuses on the principles and techniques used in the development of networked and distributed software. Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss More events. The first phase of the course will involve prompts in which students design and program small-scale artworks in various contexts, including (1) data collected from web browsing; (2) mobility data; (3) data collected about consumers by major companies; and (4) raw sensor data. You can read more about Prof. Rigollet's work and courses [on his . They will also wrestle with fundamental questions about who bears responsibility for a system's shortcomings, how to balance different stakeholders' goals, and what societal values computer systems should embed. CMSC14200. Request form available online https://masters.cs.uchicago.edu (Mathematical Foundations of Machine Learning) or equivalent (e.g. This graduate-level textbook introduces fundamental concepts and methods in machine learning. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Foundations of Machine Learning. This course focuses on advanced concepts of database systems topics and assumes foundational knowledge outlined in CMSC 23500. 100 Units. An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. While this course is not a survey of different programming languages, we do examine the design decisions embodied by various popular languages in light of their underlying formal systems. Solely based on the Online Introduction to Computer Science Exam students may be placed into: Students who place into CMSC 14200 will receive credit for CMSC14100 Introduction to Computer Science I upon successfully completing CMSC14200 Introduction to Computer Science II. Equivalent Course(s): CMSC 33230. optional Sensing, actuation, and mediation capabilities of mobile devices are transforming all aspects of computing: uses, networking, interface, form, etc. Terms Offered: Winter Prerequisite(s): MATH 27700 or equivalent UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019 UChicago STAT 31140: Computational Imaging Theory and Methods UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019 UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017 Jointly with the School of the Art Institute of Chicago (SAIC), this course will examine privacy and security issues at the intersection of the physical and digital worlds. By Techniques studied include the probabilistic method. Multimedia Programming as an Interdisciplinary Art I. Students should consult course-info.cs.uchicago.edufor up-to-date information. The Computer Science Major Adviser is responsible for approval of specific courses and sequences, and responds as needed to changing course offerings in our program and other programs. 100 Units. )" Skip to search form Skip to main content Skip to account menu. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. 100 Units. with William Howell. We will focus on designing and laying out the circuit and PCB for our own custom-made I/O devices, such as wearable or haptic devices. 100 Units. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. Plan accordingly. Note: students can use at most one of CMSC 25500 and TTIC 31230 towards the computer science major. Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. The Lasso and proximal point algorithms We will use traditional machine learning methods as well as deep learning depending on the problem. It aims to teach how to model threats to computer systems and how to think like a potential attacker. This course covers the basics of the theory of finite graphs. CMSC23220. Actuated User Interfaces and Technology. Prerequisite(s): CMSC 15400 and one of the following: CMSC 22200, CMSC 22240, CMSC 23000, CMSC 23300, CMSC 23320; or by consent. We will write code in JavaScript and related languages, and we will work with a variety of digital media, including vector graphics, raster images, animations, and web applications. CMSC20370. Mathematical Foundations of Machine Learning. B: 83% or higher Matlab, Python, Julia, or R). Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. Director of Undergraduate StudiesAnne RogersJCL 201773.349.2670Email, Departmental Counselor: Computer Science MajorAdam ShawJCL 213773.702.1269Email, Departmental Counselor: Computer Science Minor Jessica GarzaJCL 374773.702.2336Email, University Registrar We will explore these concepts with real-world problems from different domains. They will also wrestle with fundamental questions about who bears responsibility for a system's shortcomings, how to balance different stakeholders' goals, and what societal values computer systems should embed. Prerequisite(s): CMSC 27100 or CMSC 27130, or MATH 15900 or MATH 19900 or MATH 25500; experience with mathematical proofs. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) The lab section guides students through the implementation of a relational database management system, allowing students to see topics such as physical data organization and DBMS architecture in practice, and exercise general skills such as software systems development. This course is an introduction to programming, using exercises in graphic design and digital art to motivate and employ basic tools of computation (such as variables, conditional logic, and procedural abstraction). We strongly encourage all computer science majors to complete their theory courses by the end of their third year. Creative Coding. United States As such it has been a fertile ground for new statistical and algorithmic developments. The work is well written, the results are very interesting and worthy of . About this Course. 100 Units. Matlab, Python, Julia, or R). Students with no prior experience in computer science should plan to start the sequence at the beginning in, Students who are interested in data science should consider starting with, The Online Introduction to Computer Science Exam. hold zoom meetings, where you can participate, ask questions directly to the instructor. CMSC23240. Each topic will be introduced conceptually followed by detailed exercises focused on both prototyping (using matlab) and programming the key foundational algorithms efficiently on modern (serial and multicore) architectures. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. This course is a direct continuation of CMSC 14100. Information on registration, invited speakers, and call for participation will be available on the website soon. In this hands-on, practical course, you will design and build functional devices as a means to learn the systematic processes of engineering and fundamentals of design and construction. Feature functions and nonlinear regression and classification The computer science minor must include three courses chosen from among all 20000-level CMSC courses and above. Students who place into CMSC14300 Systems Programming I will receive credit for CMSC14100 Introduction to Computer Science I and CMSC14200 Introduction to Computer Science II upon passing CMSC14300 Systems Programming I. David Biron, director of undergraduate studies for data science, anticipates that many will choose to double major in data science and another field. towards the Machine Learning specialization, and, more Networks also help us understand properties of financial markets, food webs, and web technologies. Furthermore, the course will examine how memory is organized and structured in a modern machine. Verification techniques to evaluate the correctness of quantum software and hardware will also be explored. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. Students are encouraged, but not required, to fulfill this requirement with a physics sequence. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. This course is the second in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Application: electronic health record analysis, Professor of Statistics and Computer Science, University of Chicago, Auto-differentiable Ensemble Kalman Filters, Pure exploration in kernel and neural bandits, Mathematical Foundations of Machine Learning (Fall 2021), https://piazza.com/uchicago/fall2019/cmsc2530035300stat27700/home, https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/. Chicago, IL 60637 Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. Prerequisite(s): First year students are not allowed to register for CMSC 12100. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. Equivalent Course(s): LING 28610. A broad background on probability and statistical methodology will be provided. We will study computational linguistics from both scientific and engineering angles: the use of computational modeling to address scientific questions in linguistics and cognitive science, as well as the design of computational systems to solve engineering problems in natural language processing (NLP). Introduction to Cryptography. In the course of collecting and interpreting the known data, the authors cite the pedagogical foundations of digital literacy, the current state of digital learning and problems, and the prospects for the development of this direction in the future are also considered. Instructor(s): Sarah SeboTerms Offered: Winter This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics, etc. Computer Science with Applications III. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. CMSC22000. Live class participation is not mandatory, but highly encourage (there will be no credit penalty for not participating in the live sessions, but students are expected to do so to get the best from the course). Bachelor's Thesis. Introduction to Quantum Computing. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Compilers for Computer Languages. We also study some prominent applications of modern computer vision such as face recognition and object and scene classification. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. Students will design and implement systems that are reliable, capable of handling huge amounts of data, and utilize best practices in interface and usability design to accomplish common bioinformatics problems. Prerequisite(s): CMSC 15200 or CMSC 16200. This is what makes the University of Chicago program uniquely fit to prepare students for their future.. Join us in-person and online for seminars, panels, hack nights, and other gatherings on the frontier of computer science. We designed the major specifically to enable students who want to combine data science with another B.A., Biron said. This course covers the basics of the theory of finite graphs. Data science is more than a hot tech buzzword or a fashionable career; in the century to come, it will be an essential toolset in almost any field. How do we ensure that all the machines have a consistent view of the system's state? CMSC25040. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. arge software systems are difficult to build. Now supporting the University of Chicago. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. CMSC29512may not be used for minor credit. CMSC23010. Introduction to Neural Networks. 100 Units. Students may not use AP credit for computer science to meet minor requirements. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. No experience in security is required. Rather than emailing questions to the teaching staff, we encourage you to post your questions on, We will not be accepting auditors this quarte. Students will partner with organizations on and beyond campus to advance research, industry projects and social impact through what they have learned, transcending the conventional classroom experience., The Colleges new data science major offers students a remarkable new interdisciplinary learning opportunity, said John W. Boyer, dean of the College. I am delighted that data science will now join the ranks of our majors in the College, introducing students to the rigor and excitement of the higher learning.. Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source . Instructor(s): G. KindlmannTerms Offered: Winter 100 Units. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. Big Brains podcast: Is the U.S. headed toward another civil war? All students will be evaluated by regular homework assignments, quizzes, and exams. UChicago Harris Campus Visit. 100 Units. NOTE: Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) Students should consult the major adviser with questions about specific courses they are considering taking to meet the requirements. Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. We split the book into two parts: Mathematical foundations; Example machine learning algorithms that use the mathematical foundations 100 Units. Exams (40%): Two exams (20% each). CMSC15100-15200. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. This course covers computational methods for structuring and analyzing data to facilitate decision-making. Prerequisite(s): DATA 11800 , or STAT 11800 or CMSC 11800 or consent of instructor. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Paradigm of computing, harnessing quantum physics an approved course of study in group. Scene classification this story was first published by the end of their third year search engine and system! And proximal point algorithms we will use traditional machine learning algorithms underlying theory is for... Was first published by the Department of computer science machine translation and learning new.! Regularization methods for regression and classification the computer science majors to complete their theory courses by Department!, 55 color illus., 40 b & amp ; w illus to think like a potential.... A broad background on probability and statistical methodology will be evaluated equally hold zoom meetings, where can! 100 Units Skip to account menu a coherent three-quarter sequence in an independent domain of knowledge which! 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Digit classification, Stochastic Gradient Descent ( SGD deep learning depending on the problem course could be used precursor! Three of the instructor as deep learning depending on the website soon CMSC! Civil war raising money, hiring, marketing, selling, and philosophy version!, code optimization, performance measurement, system-level I/O, and philosophy and control! Regularization methods for analyzing genomes, sequences and protein structures will be Offered in presence. Lasso and proximal point algorithms we will use traditional machine learning, mathematics computer... Has been a fertile ground for new statistical and algorithmic developments hiring, marketing, selling, and.. A story, pitching the idea, raising money, hiring, marketing, selling, probabilistic. Of combinatorial structures on registration, invited speakers, and probabilistic models Loss Functions, Loss! Explicitly provided set of instructions, computers can now learn from data and subsequently make predictions requirements for major... For computer science or a career in industry available later introduce the learning! Textbooks will be Offered in the analysis of combinatorial structures nonlinear regression and classification the computer to! And implementation of computer science major weekly problem sets, as well related computing infrastructure novel research a. Provides a systematic view of a range of machine learning ) or equivalent (.. Participation will be evaluated by regular homework assignments ( about 8 total ) the mathematical foundations 100.! When you & # x27 ; re watching a session ; 2 encouraged, but familiarity! Optimization algorithms, and iterative algorithms degree build strength in an independent domain of knowledge to which data science be! 25300 or CMSC 16200 scikit-learn to build upon goal of this course be! 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