What I've Studied

Introduction to probabilistic models at the heart of modern AI. Probabilistic methods for reasoning and decision-making under uncertainty, inference and learning in Bayesian networks, prediction and planning in Markov decision processes, applications to intelligent systems.

Broad introduction to machine learning. Supervised learning, k-nearest-neighbor classifiers, decision trees, boosting, perceptrons, and unsupervised learning such as k-means and hierarchical clustering.

Fundamentals of deep neural networks: linear regression, multi-layer perceptrons, backpropagation, and automatic differentiation. Convolutional neural networks, recurrent neural networks, and transformers.

Current methods for data mining and predictive analytics. Emphasis on studying real-world data sets, building working systems, and putting current ideas from machine-learning research into practice.

Introduction to neural-network methods for analyzing linguistic data. Basic architectures and optimization via backpropagation and stochastic gradient descent. Word vectors and recurrent neural networks, and their uses and limitations in modeling the structure of natural language.

First course in data science. Data exploration, statistical inference, and prediction, using the Python programming language for tabular data manipulation, visualization, and simulation. Homework and projects on real-world datasets across a variety of domains.

The structures underlying the programs, algorithms, and languages used in data science. Taught in Python: recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists.

Practical experience composing larger computational systems through several programming projects in Java. Encapsulation, abstract data types, interfaces, algorithms and complexity, and data structures including stacks, queues, priority queues, heaps, linked lists, binary trees, BSTs, and hash tables.

First of the theoretical-foundations sequence. Mathematical theory underlying fundamental topics in machine learning: empirical risk minimization, optimization, regression, classification, and discrete probability.

Second of the theoretical-foundations sequence. Fundamentals of computer science with applications to data science: time-complexity analysis, analysis of recursive algorithms, graph theory, and graph-search algorithms.

Bridges lower- and upper-division data science. Students master the data-science life-cycle and the fundamental principles and techniques spanning algorithms, statistics, machine learning, visualization, and data systems.

Storage and management of large-scale data using classical relational (SQL) systems, with an eye toward applications in data science. SQL data model and query language, relational data modeling and schema design, cost-based query optimization, relational-database architecture, and database-backed applications.

Topics of special interest in data science. Content varies from quarter to quarter.

Concepts of data and its role in science, and the ideas behind data-mining, text-mining, machine learning, and graph theory, and how scientists and companies are leveraging those methods to uncover new insights into human cognition.

Data science is multidisciplinary, computer science, statistics, cognitive science and psychology, data visualization, AI, and machine learning. The course teaches critical skills needed to pursue a data-science career using hands-on programming and experimental challenges.

Matrix algebra, Gaussian elimination, determinants. Linear and affine subspaces, bases of Euclidean spaces. Eigenvalues and eigenvectors, quadratic forms, orthogonal matrices, diagonalization of symmetric matrices. Applications. Symbolic and graphical solutions using MATLAB.

Foundations of differential and integral calculus of one variable. Functions, graphs, continuity, limits, derivative, tangent line. Applications with algebraic, exponential, logarithmic, and trigonometric functions. Introduction to the integral.

Integral calculus of one variable and its applications, with exponential, logarithmic, hyperbolic, and trigonometric functions. Methods of integration. Infinite series. Polar coordinates in the plane and complex exponentials.

Vector geometry, vector functions and their derivatives. Partial differentiation. Maxima and minima. Double integration.

Ordinary differential equations: exact, separable, and linear; constant coefficients, undetermined coefficients, variation of parameters. Systems. Series solutions. Laplace transforms. Techniques for engineering sciences.

A variety of topics in mathematics used to introduce rigorous mathematical proof, emphasizing quantifiers, induction, negation, proof by contradiction, naive set theory, equivalence relations, and epsilon-delta proofs.

Introduction to probability. Discrete and continuous random variables, binomial, Poisson, and Gaussian distributions. Central limit theorem. Data analysis and inferential statistics: graphical techniques, confidence intervals, hypothesis tests, curve fitting.

Quantitative methods and statistical techniques for analyzing data, in particular big data. Quick review of probability, then how to process, analyze, and visualize data using the statistical language R.

Accelerated introductory programming with an object-oriented approach. Variables, conditionals, loops, functions, structured data storage, and mutation in Java; class design, interfaces, basic class hierarchies, recursion, event-based programming, and file I/O. Basics of command-line navigation for file management and running programs.

Use and implementation of basic data structures including linked lists, stacks, and queues, and of advanced structures such as binary trees and hash tables. Object-oriented design including interfaces, polymorphism, encapsulation, abstract data types, pre-/post-conditions, and recursion. Java and Java Collections.

Hands-on exploration of software-development tools and techniques. Investigation of the scientific process as applied to software development and debugging, with weekly hands-on laboratory experiences and development of laboratory-notebooking techniques as applied to software design.

Introduces the ways logic is used in computer science, for reasoning, as a language for specifications, and as operations in computation. Sets, relations, functions, equivalence relations, partial orders, number systems, and proof methods (especially induction and recursion).

Examines the ethical foundation for choices individuals make every day both in the workplace and in their private lives, the connection between economic and ethical obligations, with examples related to privacy, reporting, whistle-blowing, workplace relationships, confidentiality, and intellectual property.

Defining markets for products and services, segmenting those markets, and targeting critical customers within segments. Strategies to position products and services within segments. The critical role of pricing, as well as market research, product management, promotion, selling, and customer support.

Consider new project concepts. Discern market needs, competitive environment, and determine 'go-to-market' strategy. Research potential markets, customers, partners, and competitors. Consider price versus attributes and alternative distribution channels. Examine the need and structure of a start-up team.

Build a business plan. Establish intellectual-property rights. Provide financial projections and determine financing needs. Explore investment sourcing, business valuation, and harvesting opportunities. Determine operational plans and key employee requirements.

Focusing on how AI will redefine businesses in the digital age, studies the fundamental topics of innovation driven by the AI+X revolution, disruptive technologies, and digital transformation.

Supply-chain management involves the flows of materials and information that contribute value to a product, from the source of raw materials to end customers. Explains how supply chains work and describes the major challenges in managing an efficient supply chain.

Taking a global perspective, examines how innovation is funded and the financial tools necessary over the life cycle of a new venture, development, growth, maturity, and exit.

First of a two-quarter sequence designed to offer students who need extra English-language support and/or more time to develop their critical-thinking and writing abilities.

Second of the AWP 4A/4B sequence. Continuing English-language support and critical-thinking / writing development; a grade of C or better satisfies the UC Entry Level Writing Requirement.

Critically examines, through an interdisciplinary and antiracist lens, how we communicate about the climate crisis and how to encourage action. Students develop awareness of messaging and rhetorical context through assignments and activities.

Builds on the skills developed in Synthesis 1. Students learn connections between climate change and racial justice, conduct group research on climate aspects, and receive an introduction to project design and proposal drafting.

Project-based course that builds upon foundational skills by taking a collaborative, interdisciplinary approach to complex global problems. Each section explores different climate-related themes through team project design and execution.

Introduction to the study of the economic system. The standard economic models used to examine how individuals and firms make decisions in perfectly competitive markets, and how those decisions affect supply and demand in output markets.

Introductory macroeconomics: unemployment, inflation, business cycles, monetary and fiscal policy.

One-quarter preparatory chemistry course intended for students continuing on to general chemistry. Focuses on the development and analysis of submicroscopic models of matter and structure-property relationships to explain, predict, and control chemical behavior.

First quarter of a three-quarter sequence for science and engineering majors. Atomic theory, bonding, molecular geometry, stoichiometry, and types of reactions.

Second quarter of the science-and-engineering general-chemistry sequence. Gases, liquids, and solids; thermochemistry and thermodynamics; physical and chemical equilibria; solubility.

A rigorous analysis of symbolic systems and their interpretations. Students learn to encode and decode information using progressively more sophisticated methods, ancient and modern phonetic writing systems, hieroglyphics, computer languages, and ciphers.

Broad chronological overview of the development of hip-hop as a musical form from the late 1970s through today. Examines the style in relation to direct context and to earlier African American musical and cultural forms, and considers the technological and legal issues that have impacted its development.

Beginning course in the fundamentals of acting: establishing a working vocabulary and acquiring the basic skills of the acting process. Through exercises, compositions, and improvisations, the student actor explores the imagination as the actor's primary resource and the basic approach to text through action.