CSE190 - Topics in Computer Science and Engineering

This page updated May 6, 2013.

CSE 190 is a course in which the CSE Department introduces new topics to undergraduate students. Some such topics could then be formulated as a regular course if warranted.   

2013 - 2014 CSE 190 Courses:

Fall 2013:

CSE 190 Topics in CSE: Advanced Algorithms with Dr. Mohan Paturi: The course will focus on advanced algorithmic topics including  linear programming, randomized algorithms and approximation algorithms. The course  will focus on algorithmic techniques that are not usually covered in  CSE 101. Evaluation is by means of home work assignments, mid-term and final examinations as well as class participation.  CSE 101 is a prerequisite. All students must be cleared for enrollment.  Please email the Peer Advisers at CSEPeerAdviser@eng.ucsd.edu for course clearance. 

2012 - 2013 CSE 190 Courses:

Spring 2013: CSE190 Topics in CSE: Introduction to Mobile and Server Programming with Dr. Ganz Chockalingam: Growth of mobile has been unprecedented. Mobile programmers are in high demand and any student who is interested in developing client server mobile apps will benefit from this course.  The course will be structured into three sections. The students will work towards building a real application that will utilize material learnt in these sections. Prerequisites:  Experience in Java programming or C++ (CSE 11) and basic understanding of Web technologies such as HTML, SQL and Web Servers. All students must be cleared for enrollment.  Please email the Peer Advisers at CSEPeerAdviser@eng.ucsd.edu or call 858-534-8872. CSE 190: Mobile Apps Course Proposal

Winter 2013: CSE190 Topics in CSE: Reinforcement Learning, with Dr. Gary Cottrell 

Audience: Advanced undergrads with good programming and math skillsThere are roughly four different kinds of learning: Supervised, unsupervised, imitation, and reinforcement learning. In supervised learning, the agent is told exactly what to do in each situation, and the goal is to generalize to new situations. In unsupervised learning, the goal is to find a good representation of the data, under various metrics. In imitation learning, the agent is shown how to do something, and must learn by trying to reproduce those actions.
Reinforcement learning is probably the closest kind of learning to "real life" and is one of the most well-established mechanisms of learning in the brain.
In reinforcement learning, the agent learns by interacting with the environment, trying different actions available to it, and receives evaluative feedback in the form of a reward signal. Thus, much learning is by trial and error. The goal of the agent, like life, is to maximize the agent's long-term expected rewards. In this course, we will go through the online textbook by Sutton & Barto, Reinforcement Learning: An Introduction.

Winter 2013: CSE 190, 3D User Interaction with Dr. Jurgen Schulze:  This course focuses on the design and evaluation of three-dimensional (3D) user interfaces, devices, and interaction techniques. The course consists of a lectures, literature and hands-on work. Students will be expected to implement several techniques as part of this course.  Prereqs: CSE 167 or equivalent.  For enrollment clearance please email the Peer Advisers at CSEPeerAdviser@eng.ucsd.edu or call 858-534-8872.   CSE 190 with Dr. Schulze is connected with UC San Diego's Moxie Center.

Winter 2013: CSE 190, Android Applications with Dr. Greg Hoover:  Using Google's Android platform, students will build and launch apps.  Prereqs:  CSE 101 and CSE 110. For enrollment clearance please email the Peer Advisers at CSEPeerAdviser@eng.ucsd.edu or call 858-534-8872.  CSE 190 with Dr. Hoover is connected with UC San Diego's Moxie Center.  For complete course details see CSE 190 Course Syllabus.

Fall 2012: CSE 190, Beyond Relational Data Models with Dr. Alin Deutsch:  The course will cover modern data models that have emerged in recent years for applications whose data modeling needs go beyond classical Entity-Relationship diagrams and relational tables.  Such applications include XML data processing, and search and reasoning over Semantic Web data (ontologies).

The applications essentially view data as a graph whose nodes and/or edges are labeled, and in which both the actual labels and the existence of paths between nodes are semantically meaningufl.  We will therefore explore work on modeling and querying graph databases.  Prerequisites: Basic exposure to SQL and relational tables, as achieved in an introductory database course or through (possibly extra-curricular) project work. Famous examples that we weill treat in depth include:

• semi-structured databases and graph query languages based on regular path expressions
• XML databases and the standard query languages XPath, XQuery, XSLT
• RDF databases (RDF is a standard for modeling semantic web data) and
  the standard RDF query language SparQL.
• Java programming experience

2011 - 2012 CSE 190 Courses:

Fall 2011: CSE 190, Beyond Relational Data Models with Dr. Alin Deutsch:  The course will cover modern data models that have emerged in recent years for applications whose data modeling needs go beyond classical Entity-Relationship diagrams and relational tables.  Such applications include XML data processing, and search and reasoning over Semantic Web data (ontologies).

The applications essentially view data as a graph whose nodes and/or edges are labeled, and in which both the actual labels and the existence of paths between nodes are semantically meaningufl.  We will therefore explore work on modeling and querying graph databases.  Prerequisites: Basic exposure to SQL and relational tables, as achieved in an introductory database course or through (possibly extra-curricular) project work. Famous examples that we weill treat in depth include:

• semi-structured databases and graph query languages based on regular path expressions
• XML databases and the standard query languages XPath, XQuery, XSLT
• RDF databases (RDF is a standard for modeling semantic web data) and
  the standard RDF query language SparQL.
• Java programming experience

Winter 2012: CSE 190 A00, Programming with MATLAB with Dr. Cynthia Lee: This course will be converted into CSE 7 Programming with MATLAB before winter 2012 class begin.  Course Description: Fundamentals of computer programming and basic software design covering topics related to variables, functions, and control structures; writing, testing, and debugging programs in MATLAB.  Examples focus on scientific applications.

Winter 2012: CSE 190, Social Networks with Dr. Ramamohan Paturi: Social Networks - A course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. Topics include: how opinions, fads, and political movements spread through society; the robustness and fragility of food webs and financial markets; and the technology, economics, and politics of Web information and on-line communities. Prerequisites: The course is designed at the introductory undergraduate level with no formal prerequisites. Textbook: D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010.

Example course website: (at Cornell University)
http://www.infosci.cornell.<wbr></wbr>edu/courses/info2040/2010fa/

Winter 2012: CSE 190, Biometrics with Dr. David Kriegman:  Biometrics is the science of determining a person's identity by measuring his/her physiological characteristics. Fingerprinting, most widely known for its role in forensics, was used to sign and validate contracts in the 7th century during China's Tang Dynasty; today, laptop computers use automatic fingerprint recognition instead of passwords.  Technologies are being developed to verify or identify individuals based on measurements of the face, hand geometry, iris, retina, finger, ear, voice, speech, signature, lip motion, skin reflectance, DNA, and even body odor. In this course we will explore the advances in biometrics as well  as the computer vision and machine learning techniques behind them. Prerequisites: Linear algebra and Multivariable calculus (e.g. Math 20A & 20F), probability and statics (e.g., Math 183), a good working knowledge of C, C++ or Matlab programming.  Grading: Grading will be based on assignments, a quiz, and a final project. There will be no final exam.

Winter 2012: CSE 190, GPU Programming with Dr. Wolfgang Engel:  Course Description,
This course will cover techniques on how to implement real-time 3D graphics
techniques in an efficient way on the Graphics Processing Unit (GPU). Prerequisites: CSE 167 or comparable knowledge.

Course Description: This course focuses on algorithms and approaches for programming a GPU, including vertex, hull, tesselator, domain, geometry, pixel and compute shaders and CUDA. The list of topics is
- DirectX 11 API
- Deferred Lighting, Z Pre-Pass Renderer, Deferred Shading
- Programmable MSAA
- Post-Effect Pipeline
- Shadows (Cascaded Shadow Maps, Soft Shadows, Point Light Soft Shadows)
- GPU Particle System
- Global Illumination (Screen-Space, Reflective Shadow Maps)
- Order-Independent Transparency
- CUDA, DirectCompute
After an introduction into each of the algorithms, the students will learn step-by-step on how to implement those algorithms on the GPU.

Winter 2012: CSE 190, Reinforcement Learning with Dr. Gary Cottrell: Course Description -  
Audience: Advanced undergrads with good programming and math skills

There are roughly four different kinds of learning: Supervised, unsupervised, imitation, and reinforcement learning.

In supervised learning, the agent is told exactly what to do in each situation, and the goal is to generalize to new situations.

In unsupervised learning, the goal is to find a good representation of the data, under various metrics.

In imitation learning, the agent is shown how to do something, and must learn by trying to reproduce those actions.

Reinforcement learning is probably the closest kind of learning to "real life" and is one of the most well-established mechanisms of learning in the brain. 

In reinforcement learning, the agent learns by interacting with the environment, trying different actions available to it, and receives evaluative feedback in the form of a reward signal. Thus, much learning is by trial and error. The goal of the agent, like life, is to maximize the agent's long-term expected rewards. In this course, we will go through the online textbook by Sutton & Barto, Reinforcement Learning: An Introduction. 

There will be frequent programming assignments that will start small and build over the quarter, so that the student will have first hand knowledge of the material.

There will not be a midterm or a final.

Prerequisites: Linear algebra and multivariable calculus (e.g., Math 20A & 20F), probability and statistics (e.g., CSE103, Math 183), a good working knowledge of C, C++ or Matlab programming.

 

 Past CSE 190 topics include:

• CGI & Server-side Web Programming, Spring 1996
• Internet Technologies, Dr. Ramamohan Paturi, Spring 1997
• Advanced Web Publishing, Thomas Powell, Winter 1998
• Internet Technologies, Dr. Ramamohan Paturi, Spring 1998
• Introduction to Computer and Network Security, Bennet Yee, Spring 1998
• Mathematical Technologies Analysis of Algorithms, Dr. Ronald Graham, Winter 1999
• Hot Java Web Browser and Application, Thomas Powell, Fall 1999
• Mathematical Programming, Dr. Te C. Hu, Winter 2000
• Honors: Seminar in Computers in Society, Dr. Charles Elkan, Spring 2000
• Advanced UNIX Programming, Dr. Charles Elkan, Spring 2000
• Honors: High Performance Computing, Dr. Sidney Karin, Spring 2000
• Website Design and Engineering, Thomas Powell, Fall 2000 (CSE 134A)
• Interesting Algorithms, Dr. Te C. Hu, Winter 2001
• Social and Ethical Issues in Information Technology, Dr. Te C. Hu, Winter 2001
• Software System Design and Implemenation, Dr. Geoffrey Voelker, Spring 2001 (CSE 125)
• Aspects of Supercomputing, Dr. Reagan Moore, Spring 2001
• Honors: Seminar in Computers in Society, Dr. Charles Elkan, Spring 2001
• Software Testing, Dr. William Howden, Spring 2001 (CSE 111)
• Website Design and Engineering, Thomas Powell, Fall 2001 (CSE 134A)
• Interesting Algorithms, Dr. Te C. Hu, Winter 2002
• System Internet E-Commerce, Dr. Te C. Hu, Winter 2002
• Image Processing, Dr. Serge Belongie, Winter 2002 (CSE 166)
• Ethical, Legal and Computer Science Issues, Dr. Sidney Karin, Spring 2002
• Software System Design and Implemenation, Dr. Geoffrey Voelker, Spring 2001 (CSE 125)
• Applications in Ubiquitous Computing, Dr. William Griswold, Fall 2002 (CSE 118)
• Advanced Programming, Dr. Bradley Calder, Winter 2003
• Software System Design and Implemenation, Dr. Geoffrey Voelker, Spring 2002 (CSE 125)
• Introduction to Computer Vision, Dr. David Kriegman, Spring 2003 (CSE 152)
• Graphics II: Image Synthesis, Dr. Henrik Jensen, Spring 2003
• Applications in Ubiquitous Computing, Dr. William Griswold, Fall 2003 (CSE 118)
• Programming Challenges, Dr. Bradley Calder, Winter 2005
• Research Training in Bioinformatics, Dr. Eleazer Eskin, Winter 2005
• Applied Probability and Statistics (Matlab), Dr. Yoav Freund, Spring 2006 (CSE 103)
• Biometrics, Dr. David Kriegman, Fall 2006
• Projects in Vision and Learning, Dr. Serge Belongie, Winter 2007
• Applied Probability and Statistics (Matlab), Dr. Yoav Freund, Winter 2007 (CSE 103)
• Tools and Techniques Lab, Dana Dahlstrom, Fall 2007 (CSE 15L)
• Projects in Vision and Learning, Dr. Serge Belongie, Winter 2008
• Computational Linear Algebra, Dr. Sanjoy Dasgupta, Spring 2009
• Cognitive Modeling, Dr. Gary Cottrell, Fall 2009 (CSE 153)
• XML Query, Dr. Alin Deutsch, Fall 2009
• Projects in Vision and Learning, Dr. Serge Belongie, Winter 2010
• GPU Programming, Dr. Wolfgang Engel, Winter 2010
• Biometrics, Dr. David Kriegman, Winter 2010
• Cognitive Modeling, Dr. Gary Cottrell, Fall 2009 (CSE 153)
• Social Networks, Dr. Ramamohan Paturi, Winter 2011
• Computer Vision and Machine Learning, Dr. Serge Belongie, Spring 2011 (CSE 155)
• Human Computer Interaction, Dr. James Hollan, Spring 2011
• Beyond Relational Data Models, Dr. Alin Deutsch, Fall 2011