# Computer Science

**Department Website:**

*https://www.haverford.edu/computer-science*

Computer science is the representation and manipulation of information; it is the study of the theory, analysis, design, and implementation of the data structures that represent information and the algorithms that transform them. Computer science is interdisciplinary, with roots in mathematics, physics, and engineering, and with applications in virtually every academic discipline and professional enterprise.

Computer science at Haverford College covers these fundamental concepts, with emphasis on depth of thought, clarity of expression and attention to ethical impact. This approach is consistent with the principles of scientific education in the liberal arts. Our aim is to provide students with a base of skills and capabilities that support a wide variety of post-graduation goals, rather than to follow short-term fashions and fluctuations in computer hardware and software.

## Learning Goals

Each student in computer science will be able to:

**Realize their full ability to think deeply.**This involves mastering discipline-specific concepts such as abstraction, correctness, and complexity, and recognizing their broad and deep applications, both theoretically and practically, in new contexts.- Identify the role of abstraction in a computational problem situation; for example, distinguish a general problem from a specific problem instance, or understand the mapping between an abstract data type (ADT) and a given representation of that ADT.
- Develop original, correct solutions demonstrating an appropriate level of abstraction, using two or more design techniques specific to the field.
- Express a general solution in an appropriate programming language.
- Analyze and compare the efficiency of alternative solutions, both quantitatively and qualitatively.
- Increase confidence in a solution through a variety of approaches, including code review, testing, and mathematical reasoning.

**Communicate their thinking clearly and effectively.**This involves taking a discovered or developed solution (or a given problem definition, etc.) and sharing that solution with peers, managers, clients, and other professionals, in a complete and persuasive manner, and with appropriate use of vocabulary and other tools (e.g., charts, proofs, demonstrations).**Identify, interpret and evaluate the theoretical, practical, and ethical implications of their work in the field.**This work is most easily identified as software, but other results might be papers written and published, projects chosen over others ignored, and even questions raised during the software development process.

Haverford’s Institutional Learning Goals are available on the President’s website, at *http://hav.to/learninggoals*.

## Curriculum

Computer science offers:

- a major.
- a concentration for mathematics majors.
- a minor.

**NB: Neither the concentration nor the minor will be available to students admitted for the fall of 2019 and beyond; for prior classes, these programs will remain available on an "if space permits" basis.**

Computer science also contributes substantially to the Concentration in Scientific Computing. More information on this concentration can be found on the program’s website (*https://www.haverford.edu/scientific-computing*) or catalog entry.

The major in computer science is designed for students who wish to explore fundamental questions about computation and the role of computation in society. As part of this exploration, we provide many opportunities for students to design, implement, and analyze algorithms and data structures, and develop a larger-scale hardware/software system over the course of multiple semesters. These opportunities include both individual projects and group work, and provide experience with a variety of programming languages and with computer hardware. The senior experience, and the final projects in many classes, provide opportunities for students to explore their own interests in computer science.

## Major Requirements

The major program covers the foundations of the discipline and provides a range of elective opportunities. While the computer science major is inspired by guidance from existing professional societies in computing, it is uniquely “Haverfordian” in its emphasis on a collaborative approach to a rigorous field of inquiry.

Requirements are:

- Introduction: CMSC H105 and CMSC H106, or CMSC H107, or Bryn Mawr equivalents
- A 200-level + 300-level sequence in each of the three tracks, which would include the following courses:
- Electives (two courses): one CMSC course at the 200+ level, one CMSC course at the 300+ level
- Thesis (one course): CMSC H399

A maximum of two courses for the major can be transferred from outside the Quaker Consortium, and the introductory sequence must be taken at the Bi-Co, and CMSC H240, CMSC H245, and either CMSC H340 or CMSC H345, must be taken within the Quaker Consortium.

Requests for exception must be pre-approved by the Chair of the Department.

## Senior Thesis

The senior thesis in computer science is a capstone experience under the guidance of a faculty member. Students complete a thorough literature review in the initial term, and can continue with a research project into the subsequent term. Oral, poster and written presentations are required. This experience can include original work, but it must demonstrate deep thinking and an original exposition of an advanced topic.

Students are required to enroll in a one-credit senior seminar course in the Fall term to ensure that they successfully complete this graduation requirement. There is a series of class activities and deadlines to help keep students on track for completing their thesis. In the fall semester, these include: the advisor selection process; submitting the topic proposal; completing the literature review; and the public poster presentation. In the optional spring semester, these include: implementing their project proposed in the previous term; completing a rough draft of their thesis; rehearsing their oral presentations; submitting the final thesis document; and giving their oral presentation. A second reader provides feedback periodically to the student and their advisor as to whether progress is satisfactory.

A detailed schedule is provided to all students in the seminar at the beginning of the year.

### Senior Project Learning Goals

The thesis work culminates in the writing and oral presentation of a paper. The student must also demonstrate the research skills required to produce this paper, in accordance with departmental deadlines.

An undergraduate senior paper may or may not include original research, but must present an in-depth exploration of a topic in computer science (with particular focus on understanding and evaluating some element of the computer science literature). The paper should demonstrate the student’s ability to apply, in a new context, the fundamental themes and objectives that connect all computer science classes, such as:

- separating a problem definition from its solution.
- describing clearly a proposed solution (typically with examples).
- understanding the correctness and applicability of a proposed solution.
- comparing several proposed solutions in terms of clarity, resource requirements, etc.

It is common for the thesis to center on a particular algorithm or computing system, and present the correctness and/or computational complexity thereof. However, this is not required. Students have successfully pursued other topics, such as human-computer interaction. The one core requirement is that the student demonstrates the ability to think deeply and communicate clearly about a computer science topic beyond the depth covered in classes.

The written thesis often resembles a review article, which explores in depth a collection of primary source articles from a single research group, or a survey article, which compares primary source articles from different origins.

The oral presentation is given after the thesis has been completed, though preliminary presentations are often also given as practice (and for formative assessment) during the year. The presentation is not graded, although all students are required to give one.

The learning goals for the research that goes into the thesis experience are as follows:

*Aspirational (for the best students):*

A substantial written contribution that demonstrates original thinking and/or insight about a research area inside computer science, under the supervision of a faculty member. This should include a full literature review, appropriate replication of existing work, and either:

- a clear hypothesis (model), validation (proof/experiments), and analysis; or
- original expository work, including the extension of a proof, or a new proof of an existing theorem.
- Since such theses include original material, they may constitute part of a publication (typically a joint publication with the advisor). However, publication is not required.

*Achievable (for most students):*

A confirmation and reiteration of existing work with an incremental contribution. Specifically, this includes a full literature review and either:

- a good and complete confirmation of an existing experiment on new data, including a good analysis; or
- an exposition of non-trivial graduate-level published work, including an existing proof or deep explanation of its extension/applicability (or its lack of extension) to other related concepts.

*Required (of all students):*

A non-trivial literature review/exposition of existing graduate-level published work, specifically:

The introductory material must be:

- readable by someone who has understood only the core computer science undergraduate material (e.g., programming languages, hardware, theory, algorithms, and at least one intensive systems course such as compilers or operating systems).
- detailed enough to be clear to someone within the field.

The discussion of related work should:

- include all the important related/foundational work.
- clearly identify what problem is being addressed by each work (possibly one statement of this for many/all the works).
- clearly state the basic approach being taken.
- explain how each paper supports/evaluates its own results (proof/empirical-study/ad-hoc argument).
- make clear how this work relates to the thesis itself.
- in at least one case, really address the details of how the approach works (possibly several such discussions will be needed to address the point above).

### Senior Thesis Assessment

The grade is approximately 75% based on the work done under the supervision of the faculty advisor and about 25% based on meeting the deadlines of and participating in the senior seminar, including the fall poster and spring presentation.

The senior paper is primarily assessed by the student’s advisor. Usually one or more other members of the department also read the paper and provide feedback for the student and advisor. If the student has a separate subject-matter advisor at another institution, that advisor is consulted during the grading of the paper if at all possible. All faculty involved in the thesis (and many students) are typically in attendance for the oral presentation.

The grade for the senior experience is assigned by the advisor, based on the quality of the student’s written paper (judged in terms of illustrating mastery of the learning objectives relevant to the chosen topic), on participation in the oral presentation, and on the work habits illustrated during the year’s work.

After thorough discussion by the Department, a student’s grade on the thesis will reflect how closely they have met the qualitative goals stated above. Specifically:

- 4.0: meets aspirational goals stated above.
- 3.0: meets achievable goals stated above.
- 2.0: meets required goals stated above.

All students should reach at least a 2.0 level of work on the material they submit by the end of the fall semester, and the faculty will certify students as having achieved this level (or not) in January.

In addition to submitting the written thesis document, students must also complete the assigned *presentation* elements, which typically include a December poster presentation of the thesis topic and scope, and the final oral presentation of the thesis. These presentations are graded on evidence of preparation and on participation (i.e. showing up on time for one’s own presentation, attending the rehearsals of a few others, and providing feedback and/or asking questions). Faculty will provide informal feedback to the presenters on speaking style, professionalism, diction/grammar, poise, etc., but these elements are not included in the grade.

The Computer Science minor requirements follow the same philosophy and structure as the major:

- the introductory sequence
- breadth: a 200-level course in
*each*element of the field (theory, systems, and applications) - depth: one year-long sequence (200-level into 300-level) in
*either*theory, systems, or applications

Total: 6 courses

## Minor Requirements

- CMSC H105 Introduction to Computer Science
*or*CMSC H107*or*Bryn Mawr CMSC B113. - CMSC H106 Introduction to Data Structures
*or*CMSC H107*or*Bryn Mawr CMSC B151. - CMSC H231 Discrete Mathematics
- Students with strong backgrounds in mathematics and prior knowledge of the topics covered in CMSC H231 may wish to seek instructor permission to place into CMSC H340 /CMSC H345 without prior completion of CMSC H231—in this case, the student may complete the requirements for the minor with another course covering discrete mathematics, from the following list: MATH H210 (Linear Optimization), MATH H394 (Logic), MATH H394 (Cryptography), MATH H395 (Combinatorics), or STAT H203, STAT H218, STAT H286, or STAT H396.

- CMSC H251 Principles of Computing Systems
- CMSC H260 Foundations of Data Science
- One 300-level core course from the following list

## Concentration Requirements

The Computer Science Department supports the Concentration in Scientific Computing, available to a variety of majors (*https://www.haverford.edu/scientific-computing*), and provides a computer science concentration specific to mathematics majors.

### Computer Science Concentration for Mathematics Majors Requirements

#### NB: This concentration will not be available to students admitted for the fall of 2019 and beyond; for prior classes, these programs will remain available on an "if space permits" basis.

- CMSC H105 (Introduction to Computer Science)
*and*CMSC H106 (Introduction to Data Structures), or CMSC H107. *Either*CMSC H240 (Principles of Computer Organization)*or*CMSC H245 (Principles of Programming Languages).*Either*CMSC H340 (Analysis of Algorithms)*or*CMSC H345 (Theory of Computation).- One cross-listed MATH/CMSC course (Note that CMSC H231 meets this requirement and is the prerequisite for CMSC H340 and CMSC H345.)
- One additional 300-level computer science course.

## Affiliated Faculty

**Soheil Anbouhi**

Visiting Assistant Professor of Mathematics and Statistics

**Christopher Braun**

Visiting Assistant Professor of Computer Science

**Jane Chandlee**

Associate Professor and Haverford Chair of Linguistics (TriCo)

**Pritam Choudhury**

Visiting Assistant Professor of Computer Science

**John Dougherty**

Associate Professor of Computer Science

**Rebecca Everett**

Associate Professor of Mathematics and Statistics

**Sorelle Friedler**

The Shibulal Family Computer Science Professor; Associate Professor of Computer Science

**Alvin Grissom**

Assistant Professor of Computer Science

**Suzanne Lindell**

Amanuensis

**Steven Lindell**

Professor of Computer Science

**David Lippel**

Visiting Assistant Professor of Mathematics and Statistics

**Robert Manning**

Professor of Mathematics and Statistics; William H. and Johanna A. Harris Chair of Computational Science

**Sara Mathieson**

Assistant Professor of Computer Science

**David Wonnacott**

Professor and Chair of Computer Science; Coordinator of Scientific Computing

**Yuxin Zhou**

Visiting Assistant Professor of Computer Science

## Faculty at Bryn Mawr

**Deepak Kumar**

Professor of Computer Science

**Aline Normoyle**

Assistant Professor of Computer Science

**Adam Poliak**

Assistant Professor of Computer Science

**Ann Moskol**

Visiting Professor Computer Science

**Geoffrey Towell**

Visiting Assistant Professor of Computer Science

**Dianna Xu**

Chair and Professor of Computer Science

## Courses

NB: Bryn Mawr courses are described at *https://www.brynmawr.edu/cs/courses*

**CMSC H105 INTRODUCTION TO COMPUTER SCIENCE (1.0 Credit)**

*Christopher Braun, Suzanne Lindell, Yuxin Zhou*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

Introduction to the intellectual and software tools used to create and study algorithms: formal and informal problem specification; problem solving and algorithm design techniques; reliability, formal verification, testing, and peer code review techniques; program clarity, complexity and efficiency; functional and imperative paradigms; associated programming skills. Students must attend a one-hour weekly lab. Labs will be sectioned by course professor. Prerequisite(s): May not be taken by students who have taken any one of HC: CMSC 104, CMSC 107; BMC: CMSC 110, except by instructor consent

(**Offered:** Fall 2023)

**CMSC H106 INTRODUCTION TO DATA STRUCTURES (1.0 Credit)**

*Christopher Braun*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

An introduction to the fundamental data structures of computer science: strings, lists, stacks, queues, trees, BSTs, graphs, sets and their accompanying algorithms. Principles of algorithmic analysis and object reasoning and design will be introduced using mathematical techniques for the notions of both complexity and correctness. More practical issues, such as memory management and hashing, will also be covered. The programming language used to illustrate and implement these concepts will be able to support functional, imperative and object-oriented approaches. Emphasis will be placed on recursive thinking and its connection to iteration. Students must attend a one-hour weekly lab. Labs will be sectioned by course professor. Prerequisite(s): CMSC 105 (or 110 or 113 at Bryn Mawr) or instructor consent; may not be taken by students who have taken any one of HC: CMSC 104, CMSC 107; BMC: CMSC 206, CMSC 151, except by instructor consent

(**Offered:** Fall 2023, Spring 2024)

**CMSC H107 INTRODUCTION TO COMPUTER SCIENCE AND DATA STRUCTURES (1.0 Credit)**

*David Wonnacott, Suzanne Lindell*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

An accelerated treatment of CMSC 105/106 for students with significant programming experience. Reviews programming paradigms, while focusing on techniques for reasoning about about software: methodical testing, formal verification, code reviews, other topics as time permits. Includes lab work. Prerequisite(s): CMSC104 or instructor consent, or placement by CS faculty, based on CS placement test. If you are interested in CMSC 107, you should preregister for the CMSC 105 section at the same time and take the placement test by the deadline, typically Wednesday before classes start; may not be taken by students who have taken any one of HC: CMSC 105, CMSC 106; BMC: CMSC 206, except by instructor consent

(**Offered:** Fall 2023)

**CMSC H208 SPEECH SYNTHESIS AND RECOGNITION (1.0 Credit)**

*Jane Chandlee*

**Division:** Natural Science; Symbolic Reasoning **Domain(s):** C: Physical and Natural Processes

An introduction to the methodologies used in the automated recognition and synthesis of human speech, focusing on Hidden Markov Models in recognition and unit selection in synthesis. Students will get hands-on experience with implementing the various components of these systems to better understand the techniques, challenges, and open areas of research. Crosslisted: Computer Science, Linguistics Prerequisite(s): LING 204, CS105 and 106 OR CS107 OR BMC 110 and 206 OR instructor consent

**CMSC H209 MORPHOLOGICAL REINFLECTION (1.0 Credit)**

*Jane Chandlee*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

Morphological reinflection is the machine learning problem of developing automated tools to convert a word marked for one category into its corresponding form in another category (e.g., running to ran). The difficulty varies with the complexity of the language’s inflectional system and the resources available for that language. We will study current methods for solving this problem, using many example languages to understand what aspects of linguistic structure prove most challenging for this computational task. Crosslisted: Linguistics, Computer Science Pre-requisite(s): LING 204 OR CMSC 104 OR CMSC 105/106 OR CMSC 107 (or BMC equivalents). LING 101 and SWAT LING 043 are desirable, but not required.

**CMSC H222 SCIENTIFIC COMPUTING: CONTINUOUS SYSTEMS (1.0 Credit)**

*Rebecca Everett*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

A survey of major algorithms in modern scientific computing, with a focus on continuous problems. Topics include numerical differentiation and integration, numerical linear algebra, root-finding, optimization, Monte Carlo methods, and discretization of differential equations. Basic ideas of error analysis are presented. Regular computer work in class introduces students to the software package Matlab, in which the algorithms are implemented and applied to various problems in the natural and social sciences. Crosslisted: Mathematics, Computer Science Prerequisite(s): Math 121

(**Offered:** Spring 2024)

**CMSC H231 DISCRETE MATHEMATICS (1.0 Credit)**

*Yuxin Zhou*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

An introduction to discrete mathematics with strong applications to computer science. Topics include set theory, functions and relations, propositional logic, proof techniques, difference equations, graphs, and trees. Co-requisite(s): CMSC 105, 107, or B110 or B113 or instructor consent

(**Offered:** Fall 2023, Spring 2024)

**CMSC H240 PRINCIPLES OF COMPUTER ORGANIZATION (1.0 Credit)**

*John Dougherty*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

Treatment of the hierarchical design of modern digital computers: boolean logic/algebra; truth tables; combinational and sequential circuits; state systems; register machines; instruction sets; memory organization; assembly language programming. Lectures cover the theoretical aspects of system architecture; labs provide implementation experience via a hardware simulator. Concurrent enrollment in this and two other CMSC lab courses requires permission of the instructor. Prerequisite(s): CMSC 106, 107, 151 or 206, and CMSC/Math 231 (or instructor consent)

**CMSC H245 PRINCIPLES OF PROGRAMMING LANGUAGES (1.0 Credit)**

*David Wonnacott, Suzanne Lindell*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

Study of the design and implementation of modern programming languages: lexical and syntactic analysis; scoping mechanisms; run-time environments; implementation of structured, functional, object-oriented, and concurrent programming languages. Lectures cover theoretical foundations of language design and implementation; labs provide opportunities to both use and implement language features. Prerequisite(s): CMSC 106, or 107 or 206, and CMSC/Math 231 (or instructor consent)

**CMSC H251 PRINCIPLES OF COMPUTING SYSTEMS (1.0 Credit)**

*John Dougherty*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

What actually happens when you hit "run", after writing your program? This course introduces the elements of hardware and language/O.S. software that execute a program, serving as a foundation for later work in these areas, and providing insights into computing efficiency that may be important to a wide range of programmers. Includes weekly lab exercises, on principles covered in lecture, and details from lecture and self-teaching (according to resource-use principles presented in the course). Pre-requisite(s): Both CMSC H106, H107, B151, or B206; and CMSC 231 (the latter as co-requisite) Lottery Preference: In fall: Senior CS majors, Junior CS majors, Sophomores, other Seniors, other Juniors (with 4 seats reserved for frosh) In spring: Senior CS majors, Frosh and Sophomores, Junior CS majors, other Seniors, other Juniors

(**Offered:** Fall 2023)

**CMSC H257 PROGRAMMING LANGUAGES AND SECURITY (1.0 Credit)**

*Pritam Choudhury*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

An introduction to the role programming languages can play in checking important properties of software, including those vital to security. Pre-requisite(s): CMSC 231, and either CMSC 106 or 151 Lottery Preference: 1. Senior CS majors 2. Junior CS majors 3. Sophomores 4. Computer Science minors

(**Offered:** Fall 2023)

**CMSC H260 FOUNDATIONS OF DATA SCIENCE (1.0 Credit)**

*Sara Mathieson*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

This course will introduce students to the principles of learning from data, including basic modeling, applied linear algebra, probability, statistics, and visualization. The lab component will focus on implementation and analysis in Python. Pre-requisite(s): MATH 105 or equivalent, CMSC H106/CMSC B206 (Data Structures), corequisite CMSC H231 (Discrete Math), or permission of the instructor.

(**Offered:** Fall 2023)

**CMSC H325 COMPUTATIONAL LINGUISTICS (1.0 Credit)**

*Alvin Grissom*

**Division:** Natural Science

An overview of key areas of computational linguistics, including natural language processing and computational modeling of morpho-phonological systems. Students will study and practice the primary algorithms and techniques used in the automated analysis of natural language data. Crosslisted: Computer Science, Linguistics Prerequisite(s): CMSC 105 and CMSC 106 (or CMSC 107), OR CMSC B110 and CMSC B206, OR instructor permission.

**CMSC H340 ANALYSIS OF ALGORITHMS (1.0 Credit)**

*Steven Lindell*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

Qualitative and quantitative analysis of algorithms and their corresponding data structures from a precise mathematical point of view. Performance bounds, asymptotic and probabilistic analysis, worst case and average case behavior. Correctness and complexity. Particular classes of algorithms such as sorting searching will be studied in detail. Crosslisted: Computer Science, Mathematics Prerequisite(s): CMSC 106 or 107 or B206, and 231, or instructor consent

(**Offered:** Fall 2023)

**CMSC H345 THEORY OF COMPUTATION (1.0 Credit)**

*Yuxin Zhou*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

Introduction to the mathematical foundations of computer science: finite state automata, formal languages and grammars, Turing machines, computability, unsolvability, and computational complexity. Attendance at the weekly discussion section is required. Crosslisted: Computer Science, Mathematics Prerequisite(s): (CMSC 106, 107, 151, or 206) and CMSC 231, and junior or senior standing, or instructor consent

(**Offered:** Spring 2024)

**CMSC H350 COMPILER DESIGN (1.0 Credit)**

*David Wonnacott*

**Division:** Natural Science

An introduction to compiler design, including the tools and software design techniques required for compiler construction. Students construct a working compiler using appropriate tools and techniques in a semester-long laboratory project. Lectures combine practical topics to support lab work with more abstract discussions of software design and advanced compilation techniques. Prerequisite(s): CMSC 245 or instructor consent; concurrent enrollment in this and two other CMSC lab courses requires instructor consent

**CMSC H356 CONCURRENCY AND CO-DESIGN IN OPERATING SYSTEMS (1.0 Credit)**

*David Wonnacott, John Dougherty*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

A practical introduction to the principles of shared-memory concurrent programming and of hardware/software co-design, which together underlie modern operating systems; includes a substantial laboratory component, currently using Java's high-level concurrency and the HERA architecture. Prerequisite(s): CMSC 240 or CMSC 251 or instructor consent. Concurrent enrollment in this and two other CMSC lab courses requires permission of the instructor

**CMSC H360 MACHINE LEARNING (1.0 Credit)**

*Alvin Grissom*

**Division:** Natural Science; Quantitative **Domain(s):** C: Physical and Natural Processes

To explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as TensorFlow and Keras. By the end of this course, students should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data. Prerequisite(s): CMSC 260, or CMSC 325, or instructor consent

(**Offered:** Spring 2024)

**CMSC H394 ADVANCED TOPICS IN THEORETICAL COMPUTER SCIENCE: MATH FOUNDATIONS OF MACHINE LEARNING (1.0 Credit)**

*Soheil Anbouhi*

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

An introduction to the mathematical principles behind modern machine learning algorithms. Covers advanced topics in linear algebra, vector calculus, probability theory, and optimization, with a particular focus on their relevance to machine learning tasks. We will also discuss various practical applications. Crosslisted: Mathematics, Computer Science Prerequisite(s): MATH 121 and 215, or instructor permission

(**Offered:** Fall 2023)

**CMSC H396 ADVANCED TOPICS IN MACHINE LEARNING: DEEP LEARNING FOR COMPUTER VISION (1.0 Credit)**

**Division:** Natural Science **Domain(s):** C: Physical and Natural Processes

Content varies by semester; course may sometimes have a specific subtitle, but normally focuses on machine learning itself or on related content of importance to students who have completed the regular machine learning courses. Pre-requisite(s): CMSC H260 and either CMSC H360 or CMSC H325 Lottery Preference: Senior CMSC majors; other CMSC majors; others

**CMSC H399 SENIOR THESIS (1.0 Credit)**

*Steven Lindell*

**Division:** Natural Science

Fall seminar required for seniors writing theses, dealing with the oral and written exposition of advanced material. Lottery Preference(s): Senior standing

(**Offered:** Fall 2023)

**CMSC H480 INDEPENDENT STUDY (1.0 Credit)**

**Division:** Natural Science

Independent study, supervised by a member of the Computer Science department. Prerequisite(s): Instructor consent

(**Offered:** Fall 2023)