Spring 2021 (current semester)
This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Topics covered include linear and logistic regression, SVMs, PCA and dimensionality reduction, EM and HMMs, and deep learning.
Spring 2021 (current semester)
How do you optimally encode a text file?
How do you find shortest paths in a map? How do you design a communication
network? How do you route data in a network? What are the limits of efficient
computation? This course gives a comprehensive introduction to design and analysis
of algorithms, and answers along the way to these and many other interesting
computational questions. You will learn about problem-solving; advanced data
structures such as universal hashing and red-black trees; advanced design
and analysis techniques such as dynamic programming and amortized analysis;
graph algorithms such as minimum spanning trees and network flows;
NP-completeness theory; and approximation algorithms.
Data Structures and Algorithms
This is a course about Algorithms and Data Structures using the Java programming language.
We introduce the basic concepts about complexity of an algorithm and methods on how to compute the running time of algorithms.
Then, we describe data structures like stacks, queues, maps, trees, and graphs, and we construct efficient algorithms based on these representations.
The course builds upon existing implementations of basic data structures in Java and extends them for the structures like trees, studying the performance of
operations on such structures, and the efficiency when used in real-world applications. A large project introducing students to the challenges of software
engineering concludes the course.
Computer Organization and Design
Spring 2021 (current semester)
This is the second computer organization course
and focuses on computer hardware design. Topics covered are:
(1) basic digital system design including finite state machines,
(2) instruction set design and simple RISC assembly programming,
(3) quantitative evaluation of computer performance,
(4) circuits for integer and floating-point arithmetic,
(5) datapath and control,
(8) storage hierarchy and virtual memory,
(10) different forms of parallelism including instruction level parallelism, data-level parallelism using both vectors and message-passing multi-processors, and thread-level parallelism using shared memory multiprocessors. Basic cache coherence and synchronization.
Introduction to Computer Architecture
The goal of this course is to teach you how a computer really works. We begin by discussing transistors, the basic switching elements that constitute modern computers. We then describe how these transistors can be aggregated into more complex units like gates and ALUs and ultimately datapaths that perform computation. Once we have described how we can build a computer we will move on to talking about assembly language and how the computer is programmed at the lowest level. We will spend the second half of the course talking about the C programming language and how the features of this language are mapped onto the lower level assembly constructs.
Programming Languages and Techniques
CIS 120 is a fast-paced introduction to the fundamental concepts of programming and software design. It introduces students to computer science by emphasizing the design aspects of programming. Topics include:
- Data types and data representation
- Abstraction, interfaces, and modularity
- Test-driven development
- Programming patterns (recursion, iteration, events, call-backs, collections, map-reduce, GUIs, …)
- Functional programming
- How and when to use mutable state
- Object-oriented programming
CIS 120 teaches programming concepts in two different languages: OCaml and Java, spending approximately equal time on how to design programs in each language.
Big Data Analytics
In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification.
Solving Hard Problems in Practice
What does Sudoku have in common with debugging, scheduling exams, and routing shipments? All of these problems are provably hard -- no one has a fast algorithm to solve them. But in reality, people are quickly solving these problems on a huge scale with clever systems and heuristics! In this course, we'll explore how researchers and organizations like Microsoft, Google, and NASA are solving these hard problems, and we'll get to use some of the tools they've built!
Python is an elegant, concise, and powerful language that is useful for tasks large and small. Python has quickly become a popular language for getting things done efficiently in many in all domains: scripting, systems programming, research tools, and web development. This course will provide an introduction to this modern high-level language using hands-on experience through programming assignments and a collaborative final application development project.
Automata, Computability, and Complexity
This course explores questions fundamental to computer science such as which problems cannot be solved by computers, can we formalize computing as a mathematical concept without relying upon the specifics of programming languages and computing platforms, and which problems can be solved efficiently. The topics include finite automata and regular languages, context-free grammars and pushdown automata, Turing machines and undecidability, tractability and NP-completeness. The course emphasizes rigorous mathematical reasoning as well as connections to practical computing problems such as text processing, parsing, XML query languages, and program verification.
Mathematical Foundations of CS
What are the basic mathematical concepts and techniques needed in computer science? This course provides an introduction to proof principles and logics, functions and relations, induction principles, combinatorics and graph theory, as well as a rigorous grounding in writing and reading mathematical proofs.
AP Computer Science A
AP Computer Science emphasizes object-oriented programming methodology with an emphasis on problem solving and algorithm development. It also includes the study of data structures and abstraction. Topics include: Primitive Types; Using Objects; Boolean Expressions and if Statements; Iteration; Writing Classes; Array; ArrayList; 2D Array; Inheritance; Recursion.
Spring 2021 (current semester)
In this course, students will study the social, political, environmental and economic context of engineering practice. Students will develop an analytical toolkit to identify and address ethical challenges and opportunities in the engineering profession, including studies of risk and safety, professional responsibility, and global perspectives. The course will begin with a foundation in the history of engineering practice and major Western ethical and philosophical theories. Students will then apply this material to both historical case studies, such as Bhopal, the NASA Shuttle Program, and Three Mile Island, as well as contemporary issues in big data, artificial intelligence, and diversity within the profession. Students will consider how engineers, as well as governments, the media, and other stakeholders, address such issues.
Critical writing seminars are small, innovative, discipline- and genre-based courses organized around a specific scholarly inquiry. These seminars focus on helping students improve their writing skills and each student is required to take one.
Introduction to Philosophy
Philosophers ask difficult questions about the most basic issues in human life. Does God exist? What can we know about the world? What does it mean to have a mind? How should I treat non-human animals? Do I have free will? This course is an introduction to some of these questions and to the methods philosophers have developed for thinking clearly about them.
The Social Contract
This is a critical survey of the history of western modern political philosophy, beginning from the Early Modern period and concluding with the 19th or 20th Century. Our study typically begins with Hobbes and ends with Mill or Rawls. The organizing theme of our inventigation will be the idea of the Social Contract. We will examine different contract theories as well as criticisms and proposed alternatives to the contract idea, such as utilitarianism. Besides the above, examples of authors we will read are Locke, Rousseau, Hume, Mill and Marx.
AP Spanish Language and Culture
This advanced course is built around four main units: family and communities, science and technology, arts and beauty, and finally, individual and community identities. These topics will be studied through three different lenses: the world, the Spanish-speaking world, and the student's individual experience. Through literary texts, newspaper articles, videos and other media, students will be introduced to these topics. Students will develop advanced listening, speaking, and expository writing skills while thoroughly reviewing Spanish grammar. The assessment focus will be less on quizzes and tests, and more on original, creative work - produced individually or in groups - such as projects and presentations. Students will present Noticiero projects on topics of personal interest and lead the class in mini discussions of their chosen topics.