2021 Remote Computer Science & Engineering Summer Courses

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 MF

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

Proposed Instructor - Patrick Tantalo

CSE 20

Session TBA

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 MF

Proposed Instructor - Dani Alves

CSE 111

Session 1

An introduction to object-oriented techniques of software development including data abstraction, inheritance, polymorphism, and object-oriented design. Extensive practice using a computer to solve problems, including construction of graphical user interfaces and a multithreaded client/server applications. (Formerly Computer Science 109.)

Prerequisite(s): CSE 15 and CSE 15L, or CMPM 35, or CSE 101.

Proposed Instructor - David Harrison

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

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

CSE 183

Session 1

The World-Wde Web is one of the main mechanisms by which computer applications are delivered to users. This course introduces the design of Web applications. Students learn the main technologies involved, and build web applications as part of homework assignments and group class projects.(Formerly CMPS 183.) General Education Code PR-E

Prerequisite(s): CSE 15 and CSE 15L, or CMPM 35, or CSE 101.

Proposed Instructor - David Harrison