2020 Computer Science & Engineering Summer Courses

CSE 13E

8-Week

Introduction to the C programming language as a means for controlling embedded computing systems. Continuing the exploration begun in course 12, students move to higher levels of abstraction in the control of complex computer systems. Students cannot receive credit for both CSE 13E and CSE 13S. Course is 7 credits with integrated laboratory. (Formerly Computer Engineering 13, Computer Systems and C Programming, and Computer Engineering 13L, Computer Systems and C Programming Lab.)

Prerequisite(s): CSE 12 and CSE 12L.

Proposed Instructor - Max Dunne

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CSE 16

8-Week

Introduction to applications of discrete mathematical systems. Topics include sets, functions, relations, graphs, predicate calculus, mathematical proof methods (induction, contraposition, contradiction), counting methods (permutations, combinations), and recurrences. Examples are drawn from computer science and computer engineering. Knowledge of computer programming is useful before taking this course. Students who do not have prior programing experience are strongly recommended to take Computer Science 5C, 5J, or 5P before taking this course. (Formerly Computer Engineering 16.) (General Education Code(s): MF.)

Prerequisite(s): MATH 19A or MATH 11B or AM 11B or AM 15B or ECON 11B.

Proposed Instructor - Patrick Tantalo

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CSE 20

8-Week

Provides students with Python programming skills and the ability to design programs and read Python code. Topics include data types, control flow, methods and advanced functions, built-in data structures, and introduction to OOP. No prior programming experience is required. Students may not receive credit for CSE 20 after receiving credit for CSE 30. (Formerly CMPS 5P, Introduction to Programming in Python.) (General Education Code(s): MF.)

Proposed Instructor - Larissa Munishkina

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CSE 115A-01

Session 1

Emphasizes the characteristics of well-engineered software systems. Topics include requirements analysis and specification, design, programming, verification and validation, maintenance, and project management. Practical and research methods are studied. Imparts an understanding of the steps used to effectively develop computer software. (Formerly Computer Science 115.)

Prerequisite(s): CSE 101 and CSE 130 and satisfaction of the Entry Level Writing and Composition requirements

Proposed Instructor - Richard Jullig

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CSE 115A-02

Session 1

Emphasizes the characteristics of well-engineered software systems. Topics include requirements analysis and specification, design, programming, verification and validation, maintenance, and project management. Practical and research methods are studied. Imparts an understanding of the steps used to effectively develop computer software. (Formerly Computer Science 115.)

Prerequisite(s): CSE 101 and CSE 130 and satisfaction of the Entry Level Writing and Composition requirements

Proposed Instructor - Richard Jullig

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CSE 140

Session 1

Introduction to the contemporary concepts and techniques of artificial intelligence, including any or all of: machine perception and inference, machine learning, optimization problems, computational methods and models of search, game playing and theorem proving. Emphasis may be on any formal method of perceiving, learning, reasoning, and problem solving which proves to be effective. This includes both symbolic and neural network approaches to artificial intelligence. Issues discussed include symbolic versus nonsymbolic methods, local versus global methods, hierarchical organization and control, and brain modeling versus engineering approaches. Lisp or Prolog may be introduced. Involves one major project or regular programming assignments. (Formerly CMPS 140.) Prerequisite(s): CSE 101.

Proposed Instructor - Narges Norouzi

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CSE 144

Session 1

Provides a practical and project-oriented introduction to machine learning, with an emphasis on neural networks and deep learning. Starts with a discussion of the foundational pieces of statistical inference, then introduces the basic elements of machine learning: loss functions and gradient descent. Using these, presents logistic regression, or one-layer networks, and then moves on to more complex models: deep neural networks, convolutional networks for image recognition, and recurrent networks and LSTM for temporal and sequence data. Also covers the basics of dataset preparation and visualization and the performance characterization of the models created. Includes weekly homework and a final project that can be done in groups. (Formerly CMPS 144.) Prerequisite(s): CSE 101. Enrollment is restricted to juniors and seniors.

Proposed Instructor - Narges Norouzi

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