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# 1 - 10 of 57 results for: CS106A

## AA 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 179:Orbital Mechanics and Attitude Dynamics

In this class, you will learn how to find your way in space. You will learn coordinate systems and coordinate transformations, so you will know where you are and where you are going. We will study rotational dynamics, rigid body equations of motion, their solutions and spacecraft rotational stability, so if you want to do sightseeing, you will know which direction to look. We will proceed to Newton?s law of gravity and the solution two-body problem, so you will know how to get around places. We will also cover the basics of orbital perturbations, so if someone disturbs your journey, you will not get lost. Finally, we will cover orbital maneuvers, their planning and execution, so if you want to go to multiple places, you will know when to change course, how much time it will take to get to your destination and how much it would cost. In each lecture, we will start with theory, and then proceed with applications supported by numerical examples in Python/Jupyter notebooks. During this class, we will be accumulating numerical examples that can be used together to solve progressively more complex problems in orbital and attitude dynamics. Prerequisites: CS106A for Python; ENGR 15, ( CME 100, CME 102) or ( MATH 51, MATH 53); or equivalent classes with permission of the instructor. Recommended: AA 131; if you plan to take AA179 (focus elective), it is recommended to take it before AA131 (required).
Terms: Spr | Units: 3
Instructors: Ermakov, A. (PI)

## 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

## BIODS 221:Machine Learning Approaches for Data Fusion in Biomedicine (BIOMEDIN 221)

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: 2-3

## BIOMEDIN 210:Modeling Biomedical Systems

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 (BIODS 221)

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: 2-3

## BIOMEDIN 223:Deploying and Evaluating Fair AI in Healthcare (CSRE 323, 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: 2-3

## 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 (particle in a box, particle on a ring, harmonic oscillator, rigid rotor, and hydrogen atom) and their connection to and uses in thermodynamics (laws of thermodynamics, properties of gases and thermal motion, and chemical equilibria). Homeworks and sections will employ the Python programming language for hands-on experience with simulating chemical systems. Prerequisites: CHEM 31B or CHEM 31M; PHYS 41; CS106A; and MATH 51, MATH 61CM, MATH 61DM or CME 100.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci
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