printer friendly pageAA 174B: Principles of Robot Autonomy II (AA 274B, CS 237B, EE 260B)
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.
Terms: Win
| Units: 3-4
AA 274B: Principles of Robot Autonomy II (AA 174B, CS 237B, EE 260B)
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.
Terms: Win
| Units: 3-4
BIOMEDIN 210: Modeling Biomedical Systems (CS 270)
At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites:
CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win
| Units: 3
BIOMEDIN 221: Machine Learning Approaches for Data Fusion in Biomedicine
Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent,
Stats 60 or equivalent.
Terms: Aut
| Units: 2
Instructors:
Gentles, A. (PI)
;
Gevaert, O. (PI)
BIOMEDIN 223: Deploying and Evaluating Fair AI in Healthcare (EPI 220)
AI applications are proliferating throughout the healthcare system and stakeholders are faced with the opportunities and challenges of deploying these quickly evolving technologies. This course teaches the principles of AI evaluations in healthcare, provides a framework for deployment of AI in the healthcare system, reviews the regulatory environment, and discusses fundamental components used to evaluate the downstream effects of AI healthcare solutions, including biases and fairness. Prerequisites:
CS106A; familiarity with statistics (
stats 202), BIOMED 215, or
BIODS 220
Terms: Spr
| Units: 3
Instructors:
Hernandez-Boussard, T. (PI)
BIOMEDIN 273A: The Human Genome Source Code (CS 273A, DBIO 273A)
A computational primer to "hacking" the most amazing operating system "disk" on the planet: your genome. Handling genomic data is deceptively easy. But that's muscle. You want to be the brain, too. Topics include genome sequencing (assembling source code from code fragments); the human genome functional landscape: variable assignments (genes), control-flow logic (gene regulation) and run-time stack (epigenomics); human disease and personalized genomics (as a hunt for bugs in the human code); genome editing (code injection) to cure the incurable; and the source code modifications behind amazing animal adaptations. The course will introduce ideas from computational genomics, machine learning and natural language processing. Course includes primers on molecular biology, and text processing languages. Prerequisites: CS106A or equivalent. No biological background assumed.
Terms: Win
| Units: 3
Instructors:
Bejerano, G. (PI)
;
Yoo, B. (TA)
CEE 154: Data Analytics for Physical Systems (CEE 254)
This course introduces practical applications of data analytics and machine learning from understanding sensor data to extracting information and decision making in the context of sensed physical systems. Many civil engineering applications involve complex physical systems, such as buildings, transportation, and infrastructure systems, which are integral to urban systems and human activities. Emerging data science techniques and rapidly growing data about these systems have enabled us to better understand them and make informed decisions. In this course, students will work with real-world data to learn about challenges in analyzing data, applications of statistical analysis and machine learning techniques using MATLAB, and limitations of the outcomes in domain-specific contexts. Topics include data visualization, noise cleansing, frequency domain analysis, forward and inverse modeling, feature extraction, machine learning, and error analysis. Prerequisites:
CS106A,
CME 100/
Math51,
Stats110/101, or equivalent.
Terms: Aut
| Units: 3-4
Instructors:
Noh, H. (PI)
CEE 254: Data Analytics for Physical Systems (CEE 154)
This course introduces practical applications of data analytics and machine learning from understanding sensor data to extracting information and decision making in the context of sensed physical systems. Many civil engineering applications involve complex physical systems, such as buildings, transportation, and infrastructure systems, which are integral to urban systems and human activities. Emerging data science techniques and rapidly growing data about these systems have enabled us to better understand them and make informed decisions. In this course, students will work with real-world data to learn about challenges in analyzing data, applications of statistical analysis and machine learning techniques using MATLAB, and limitations of the outcomes in domain-specific contexts. Topics include data visualization, noise cleansing, frequency domain analysis, forward and inverse modeling, feature extraction, machine learning, and error analysis. Prerequisites:
CS106A,
CME 100/
Math51,
Stats110/101, or equivalent.
Terms: Aut
| Units: 3-4
Instructors:
Noh, H. (PI)
CHEM 171: Foundations of Physical Chemistry
Quantum and statistical thermodynamics: obtaining quantum mechanical energy levels and connecting them to thermodynamic properties using statistical mechanics. Emphasis will be on quantum mechanics of ideal systems (e.g. particle in a box, particle in a ring, harmonic oscillator, hydrogen atom) and their connection to and uses in thermodynamics (laws of thermodynamics, properties of gases, chemical equilibria, thermal motion and energy barriers, and rates of chemical reactions). Homeworks and discussion sections will employ the Python programming language for hands-on experience with simulating chemical systems. Prerequisites:
CHEM 33; PHYS 41;
CS106A; either
MATH 51 or
CME 100.
Terms: Spr
| Units: 4
| UG Reqs: GER: DB-NatSci
CME 107: Introduction to Machine Learning (EE 104)
Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
ENGR 108;
EE 178 or
CS 109; CS106A or equivalent.
Terms: Spr
| Units: 3-5
Instructors:
Lall, S. (PI)
;
Soroka, E. (TA)
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