CME 243: Risk Analytics and Management in Finance and Insurance (STATS 243)
Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite:
STATS 240 or equivalent.
Terms: not given this year, last offered Summer 2016

Units: 24

Grading: Letter or Credit/No Credit
CME 244: Project Course in Mathematical and Computational Finance
For graduate students in the MCF track; students will work individually or in groups on research projects.
Terms: Aut

Units: 16

Grading: Letter (ABCD/NP)
CME 245: Topics in Mathematical and Computational Finance
Description: Current topics for enrolled students in the MCF program: This course is an introduction to computational, statistical, and optimizations methods and their application to financial markets. Class will consist of lectures and realtime problem solving. Topics: Python & R programming, interest rates, BlackScholes model, financial time series, capital asset pricing model (CAPM), options, optimization methods, and machine learning algorithms. Appropriate for anyone with a technical and solid applied math background interested in honing skills in quantitative finance. Prerequisite: basic statistics and exposure to programming.Can be repeated up to three times.
Terms: not given this year, last offered Summer 2017

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 249: Using Design for Effective Data Analysis
Teams of students use techniques in applied and computational mathematics to tackle problems with real world data sets. Application of design methodology adapted for data analysis will be emphasized; leverage design thinking to come up with efficient and effective data driven insights; explore design thinking methodology in small group setting.;apply design thinking to a specific data centric problem and make professional group presentation of the results. Limited enrollment. Prerequisites:
CME100/102/104 or equivalents, or instructor consent. Recommended:
CME106/108 and familiarity with programming at the level of
CME 192/193.
Terms: offered occasionally, last offered Winter 2016

Units: 1

Grading: Satisfactory/No Credit
CME 249A: Statistical Arbitrage
Course will cover trading strategies that are bottom up, market neutral, with trading driven by statistical or econometric models and strategies such as pair trading and index arbitrage. Models may focus on tendency of short term returns to revert, leads/lags among correlated instruments, volume momentum, or behavioral effects. nTopics include: (a) a taxonomy of market participants and what motivates trading, (b) methods of exploring relationships between instruments, (c) portfolio construction across a large number of instruments, (d) risks inherent in statistical arbitrage (e) nonstationarity of relationships due to changes in market regulations, fluctuations in market volatility and other factors and (f) frictions such as costs of trading and constraints. Students will team to analyze the provided data sets which cover distinct dynamic market regimes.
Terms: offered occasionally, last offered Spring 2016

Units: 1

Grading: Satisfactory/No Credit
CME 250: Introduction to Machine Learning
A Short course presenting the principles behind when, why, and how to apply modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of overfitting/underfitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data. The principles behind various algorithmsthe why and how of using themwill be discussed, while some mathematical detail underlying the algorithmsincluding proofswill not be discussed. Unsupervised machine learning algorithms presented will include kmeans clustering, principal component analysis (PCA), and independent component analysis (ICA). Supervised machine learning algorithms presented will include support vector machines (SVM), classification and regression trees (CART), boosting, bagging, and random forests. Imputation, the lasso, and crossvalidation concepts will also be covered. The R programming language will be used for examples, though students need not have prior exposure to R. Prerequisite: undergraduatelevel linear algebra and statistics; basic programming experience (R/Matlab/Python).
Terms: offered occasionally, last offered Spring 2017

Units: 1

Grading: Satisfactory/No Credit
CME 250A: Machine Learning on Big Data
A short course presenting the application of machine learning methods to large datasets.Topics include: brief review of the common issues of machine learning, such as, memorizing/overfitting vs learning, test/train splits, feature engineering, domain knowledge, fast/simple/dumb learners vs slow/complex/smart learners; moving your model from your laptop into a production environment using Python (scikit) or R on small data (laptop sized) at first; building math clusters using the open source H2O product to tackle Big Data, and finally to some model building on terabyte sized datasets. Prereqresites: basic knowledge of statistics, matrix algebra, and unixlike operating systems; basic file and text manipulation skills with unix tools: pipes, cut, paste, grep, awk, sed, sort, zip; programming skill at the level of CME211 or
CS106A.
Terms: offered occasionally, last offered Spring 2016

Units: 1

Grading: Satisfactory/No Credit
CME 251: Geometric and Topological Data Analysis (CS 233)
Mathematical computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces  as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for geometric data analysis. Nonlinear dimensionality reduction. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps.Networks of data sets and joint analysis for segmentation and labeling. The emergence of abstractions or concepts from data. Prerequisites: discrete algorithms at the level of 161; linear algebra at the level of
CME103.
Terms: not given this year, last offered Spring 2018

Units: 3

Grading: Letter or Credit/No Credit
CME 253: Introduction to GPU Computing and CUDA
Covers the fundamentals of accelerating applications with GPUs (Graphics Processing Units); GPU programming with CUDA and OpenACC, debugging, thrust/CUB, profiling, optimization, debugging, and other CUDA tools. Libraries to easily accelerate compute code will be presented and deployment on larger systems will be addressed, including multiGPU environments. Several practical examples will be detailed, including deep learning. Prerequiste: knowledge of C/C++ at the level of CME211 or
CS106b.
Terms: offered occasionally, last offered Winter 2017

Units: 1

Grading: Satisfactory/No Credit
CME 257: Advanced Topics in Scientific Computing with Julia
This course will rapidly introduce students to the Julia programming language, with the goal of giving students the knowledge and experience necessary to navigate the language and package ecosystem while using Julia for their own scientific computing needs. The course will begin with learning the basics of Julia, and then introduce students to git version control and package development. Additional topics include: common packages, parallelism, interfacing with shared object libraries, and aspects of Julia's implementation (e.g. core numerical linear algebra). Lectures will be interactive, with an emphasis on collaboration and learning by example. Prerequisites: Data structures at the level of
CS106B, experience with one or more scientific computing languages (e.g. Python, Matlab, or R), and some familiarity with the Unix shell. No prior experience with Julia or git is required.
Terms: Win

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Nelson, B. (PI)
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