Eric C. Larson
Associate ProfessorComputer Science
Bobby B. Lyle School of Engineering
Southern Methodist University
twitter: @ec_larson
email: eclarson@lyle.smu.edu
CS Office: 451 Caruth Hall
Lyle School of Engineering
Caruth Hall
3145 Dyer Street, Suite 445
Dallas, TX 75205
SMU UbiComp Lab:
Johnson Square 189
Courses Offered
CS5323 & CS7323 - Mobile Sensing and Learning
Course Description
This class will equip students with the practical skills necessary to develop mobile applications able to take advantage of the myriad of sensing, machine learning, and control capabilities that modern smartphones offer. The course focuses on interfacing with the hardware of the phone and inferring high level information from the sensors streams. Particular focus will be placed upon efficiently analyzing and controlling hardware peripherals on third party hardware, such as an embedded micro-controller or peripheral such as Google Glass. This third-party hardware platform will interface with the mobile platform and allow students to integrate realtime control/automation with the sensing learned earlier in the semester.Assignments will use both objective C and C++ programming languages, on the iOS platform. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
Course DescriptionThis class will equip students with the practical skills necessary to develop mobile applications able to take advantage of the myriad of sensing, machine learning, and control capabilities that modern smartphones offer. The course focuses on interfacing with the hardware of the phone and inferring high level information from the sensors streams. Particular focus will be placed upon efficiently analyzing and controlling hardware peripherals on third party hardware, such as an embedded micro-controller or peripheral such as Google Glass. This third-party hardware platform will interface with the mobile platform and allow students to integrate realtime control/automation with the sensing learned earlier in the semester.Assignments will use both objective C and C++ programming languages, on the iOS platform. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
CS5324 & CS7324 - Machine Learning in Python
Course Description
This class introduces the processes of exploring, visualizing, and classifying large amounts of data. This course provides an introduction to classic and contemporary learning techniques in classification and regression, using the Python programming language for simple APIs and rapid prototyping. We explore linear classification algorithms and their non-linear counterparts via kernel tricks. We also explore Neural Networks and deep learning architectures, with emphasis on GPU accelerated training and auto encoding procedures. Class projects focus on using Kaggle competitions as example datasets. All material covered will be reinforced through hands-on experience using state-of-the art tools to design and execute data learning algorithms. Class examples will come from Python. Pre-requisite courses for this class include basic statistics and probability, and introductory algorithm analysis (or desire to learn quickly). Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
Course DescriptionThis class introduces the processes of exploring, visualizing, and classifying large amounts of data. This course provides an introduction to classic and contemporary learning techniques in classification and regression, using the Python programming language for simple APIs and rapid prototyping. We explore linear classification algorithms and their non-linear counterparts via kernel tricks. We also explore Neural Networks and deep learning architectures, with emphasis on GPU accelerated training and auto encoding procedures. Class projects focus on using Kaggle competitions as example datasets. All material covered will be reinforced through hands-on experience using state-of-the art tools to design and execute data learning algorithms. Class examples will come from Python. Pre-requisite courses for this class include basic statistics and probability, and introductory algorithm analysis (or desire to learn quickly). Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
CS8321 - Machine Learning and Neural Networks
Course Description
Introduction to the principles and motivation behind advanced neural network design and applications. Survey of important topics and current areas of research, including transfer learning, multi-task and multi-modal learning, image style transfer, neural network visualization, generative adversarial networks, and deep reinforcement learning. Class examples and assignments will come from the programming language Python. Prerequisite: CS 7324. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
Course DescriptionIntroduction to the principles and motivation behind advanced neural network design and applications. Survey of important topics and current areas of research, including transfer learning, multi-task and multi-modal learning, image style transfer, neural network visualization, generative adversarial networks, and deep reinforcement learning. Class examples and assignments will come from the programming language Python. Prerequisite: CS 7324. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
CS5325 & CS7325 - Ubiquitous Computing
Course Description
This class explores the area of ubiquitous computing (ubicomp) and the role of cognitive computing in the evolution of the computing paradigm. The course allows students to work on a variety of small technology projects. Students will be exposed to the basics of building ubicomp systems, emerging new research topics, and advanced prototyping techniques. This course focuses more on class discussions and hands on demonstrations, while formal lectures will be conducted only as needed. Students are evaluated on their class participation, reading, papers, and projects. This course incorporates a combination of topics covering a wide variety of disciplines that impact ubiquitous computing. These include human-computer interaction (HCI), machine learning, embedded systems, signal processing, networking, and electrical engineering. While there is no explicit set of pre-requisite courses for this class, a basic introduction to a subset of these disciplines will benefit you in this class. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
Course DescriptionThis class explores the area of ubiquitous computing (ubicomp) and the role of cognitive computing in the evolution of the computing paradigm. The course allows students to work on a variety of small technology projects. Students will be exposed to the basics of building ubicomp systems, emerging new research topics, and advanced prototyping techniques. This course focuses more on class discussions and hands on demonstrations, while formal lectures will be conducted only as needed. Students are evaluated on their class participation, reading, papers, and projects. This course incorporates a combination of topics covering a wide variety of disciplines that impact ubiquitous computing. These include human-computer interaction (HCI), machine learning, embedded systems, signal processing, networking, and electrical engineering. While there is no explicit set of pre-requisite courses for this class, a basic introduction to a subset of these disciplines will benefit you in this class. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
EMIS5/7332 and CS5/7331 - Introduction to Data Mining
Course Description
This class introduces the processes of managing, exploring, visualizing, and acting on large amounts of data. This course provides an introduction to data-mining techniques (classification, regression, association and cluster analysis) used in analytics. All material covered will be reinforced through hands-on experience using state-of-the art tools to design and execute data mining processes. Class examples will come from Python and R. Pre-requisite courses for this class include basic statistics and probability, and introductory algorithm analysis (or desire to learn quickly). Experience with databases is helpful but not required. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
Course DescriptionThis class introduces the processes of managing, exploring, visualizing, and acting on large amounts of data. This course provides an introduction to data-mining techniques (classification, regression, association and cluster analysis) used in analytics. All material covered will be reinforced through hands-on experience using state-of-the art tools to design and execute data mining processes. Class examples will come from Python and R. Pre-requisite courses for this class include basic statistics and probability, and introductory algorithm analysis (or desire to learn quickly). Experience with databases is helpful but not required. Feel free to contact the instructor at eclarson@lyle.smu.edu if you have any questions.
Course Offerings
CS8098 - Research Seminar
Course Description
The Computer Science and Engineering Department at SMU hosts regular talks in the form of colloquia and distinguished lectures. Talks are held on the SMU campus and open to the public.
Course Offerings
Course DescriptionThe Computer Science and Engineering Department at SMU hosts regular talks in the form of colloquia and distinguished lectures. Talks are held on the SMU campus and open to the public.
Course Offerings
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