Master’s Degree in Bioinformatics
Advising
All academic affairs for graduate students in the program are directed by the program’s faculty graduate adviser, who is assisted by staff in the Graduate Student Affairs Office. Upon matriculation, students are assigned a three-faculty guidance committee by the faculty graduate adviser. Department Scholars in the UCLA CASB department: Please visit the DSP website for more information.
The chair of the guidance committee acts as the provisional adviser until a permanent adviser is selected. Provisional advisers are not committed to supervise examination or thesis work and students are not committed to the provisional adviser. Students select a permanent adviser before establishing a comprehensive examination or thesis committee.
Areas of Study
Field 1: Bioinformatics
This field of study provides exposure primarily to biological and algorithmic advances in genomics, proteomics, and other related fields. Study consists of a core curriculum, computer science, mathematics, and statistics. Students majoring in Computational & Systems Biology can enroll in this Field to receive their Masters degree.
Foreign Language Requirement
None.
Course Requirements
| Field | Number of required courses | Number of required units |
| Field 1: Bioinformatics | 9 | 36 |
Students in Field 1 (Bioinformatics) must be enrolled full time and complete 36 units (nine courses) of graduate (200 or 500 series) or upper division (100 series) course work for the master’s degree. Within this overall requirement, students must complete at least 20 units (five courses) at the graduate level for a letter grade. Of these five required graduate courses, four must be in the 200 series and one may be in the 500 series.
Courses must be taken for a letter grade, unless offered on S/U grading basis only.
Field 1: Bioinformatics
Students must complete all of the following: (1) Bioinformatics M229S: Current Topics in Bioinformatics; (2) Bioinformatics M223: Statistical Methods in Bioinformatics; (3) Bioinformatics M275A and B: Applied Bioinformatics; (4) Two electives from the Program’s list of approved elective courses. These two electives require the approval of the student’s PI/faculty mentor. Please note: other elective courses outside of the Program’s list can be taken with the agreement of the Home Area Director and the student’s PI/faculty mentor. (5) Bioinformatics 201 taken twice a year for two years; (6) enrollment in Bioinformatics 596 research units, although no more than two courses (eight units) of 596 may be applied toward the requirements for a master’s degree. Up to eight units (two courses) of upper division electives can be applied toward the requirements for a master’s degree.
Students in the Bioinformatics M.S. program are recommended to take the following courses in their first year:
- Core Courses: BIOINFO 275A & 275B (Fall), BIOINFO M229S (Winter), and BIOINFO M223 (Spring)
- Seminar Courses: BIOINFO 201 (Fall and Winter Quarters for their 2 years study)
Teaching Experience
Not required.
Field Experience
Not required.
Capstone Plan
The master’s capstone is an individual project in the format of a written report resulting from a research project. The report should describe the results of the student’s investigation of a problem in the area of bioinformatics under the supervision of a faculty member in the program, who approves the subject and plan of the project, as well as reading and approving the completed report. While the problem may be one of only limited scope, the report must exhibit a satisfactory style, organization, and depth of understanding of the subject. A student should normally start to plan the project at least one quarter before the award of the M.S. degree is expected. The advisory committee evaluates and grades the written report as not pass or M.S. pass and forwards the results to the faculty graduate adviser. Students who do not pass the evaluation are permitted one additional opportunity to pass, which must be submitted to and graded by the advisory committee by the end of the 6th quarter.
The capstone plan is available for students pursuing the Bioinformatics field. However, students in Computational & Systems Biology major are required to follow the Thesis Plan only.
Thesis Plan
Every master’s degree thesis plan requires the completion of an approved thesis that demonstrates the student’s ability to perform original, independent research.
Field 1: Bioinformatics
Students must choose a permanent faculty adviser and submit a thesis proposal by the end of the third quarter of study. The proposal must be approved by the permanent adviser who served as the thesis adviser. The thesis is evaluated by a three-person committee that is nominated by the program and appointed by the Division of Graduate Education. Students must present the thesis in a public seminar.
Time-to-Degree
Normative time-to-degree for all fields is five quarters.
| DEGREE | NORMATIVE TIME TO ATC (Quarters) | NORMATIVE TTD | MAXIMUM TTD |
| M.S. | 3 | 5 | 6 |
Core Bioinformatics Courses:
These are the Bioinformatics Core curriculum courses. They are the most important and relevant courses in Bioinformatics at UCLA. Students in the Bioinformatics M.S. program are required to take all four courses. For students outside the Bioinformatics M.S. or Ph.D. programs, these courses are recommended as a starting point for gaining a background in Bioinformatics. The core courses assume some knowledge of programming and a background in statistics equivalent to an upper division course.
BIOINFO M223. Statistical Methods in Computational Biology
Units: 4
(Same as Biomathematics M271 and Statistics M254.) Lecture, three hours; discussion, one hour. Preparation: elementary probability concepts. Requisite: course M260A or Statistics 100A or 200A. Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems. Letter grading.
BIOINFO M229S. Current Topics in Bioinformatics
Units: 4
Lecture, three hours; This course will introduce current research topics in bioinformatics. It will be taught by five instructors on topics of their research areas, including statistical genomics, epigenomics, single-cell analysis, metagenomics, and diagnostic informatics. Instructors will provide an overview of the research area and the associated biological and technological background. Subsequently, representative problems will be discussed, and their computational modeling and solutions will be presented. Students shall gain an overview of current topics in bioinformatics and learn how to model and solve biological problems with computational methods. Letter grading.
BIOINFO 275A. Applied Bioinformatics Lab for Biologists: Fundamentals
Units: 2
Laboratory, six hours (five weeks). Introduction to contemporary methods and techniques in bioinformatics that are used to analyze high-throughput genomic data. Topics include introduction to UNIX, Next Generation Sequence (NGS) data analysis, ChIP-seq, BS-seq and RNA-seq, and others. Letter grading. Letter grading.
BIOINFO 275B. Applied Bioinformatics Lab for Biologists: Intermediate
Units: 2
Laboratory, six hours (five weeks). Requisite: course 275A. Contemporary methods and techniques in bioinformatics that are used to analyze high-throughput genomic data. Topics include Galaxy server, R, MATLAB, Python, and variant calling. Letter grading.
Bioinformatics Seminar Course:
There is one seminar required for the M.S. in Bioinformatics at UCLA. The main seminar meets on Mondays at 12:00pm, usually in Boyer 159. The seminar information is available on the Bioinformatics website. Bioinformatics M.S. students are required to take the seminar course, which includes both the seminar as well as a meeting the week prior to discuss papers of the speaker in order to obtain background material about the seminar topics.
BIOINFO 201. Seminar: Advanced Methods in Computational Biology
Units: 2
(Same as Chemistry M252 and Human Genetics M252.) Seminar, one hour; discussion, one hour. Designed for advanced graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies. S/U or letter grading.
Other Bioinformatics Cross-Listed Courses (Electives When Available):
BIOINFO M221. Introduction to Bioinformatics
Units: 4
(Same as Chemistry CM260A, Computer Science CM221, and Human Genetics M260A.) Lecture, four hours; discussion, two hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Prior knowledge of biology not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms. S/U or letter grading.
BIOINFO M222. Algorithms in Bioinformatics and Systems Biology
Units: 4
(Same as Chemistry CM260B and Computer Science CM222.) Lecture, four hours; discussion, two hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Course M260A is not requisite to M260B. Designed for engineering students as well as students from biological sciences and medical school. Development and application of computational approaches to biological questions, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Computational techniques include those from statistics and computer science. Letter grading.
BIOINFO M224. Computational Genetics
Units: 4
(Same as Computer Science CM224 and Human Genetics CM224.) Lecture, four hours; discussion, two hours; outside study, six hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Letter grading.
BIOINFO M225. Computational Methods in Genomics
Units: 4
(Same as Computer Science M225 and Human Genetics M265.) Lecture, two and one half hours; discussion, two and one half hours; outside study, seven hours. Limited to bioinformatics, computer science, human genetics, and molecular biology graduate students. Introduction to computational approaches in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Topics include genome analysis, regulatory genomics, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genomic technologies. Computational techniques and methods include those from statistics and computer science. Letter grading.
BIOINFO M226. Machine Learning in Bioinformatics
Units: 4
(Same as Computer Science M226 and Human Genetics M226.) Lecture, four hours; outside study, eight hours. Enforced requisite: Computer Science 32 or Program in Computing 10C with grade of C- or better. Recommended: one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Familiarity with probability, statistics, linear algebra, and algorithms expected. Designed for engineering students as well as students from biological sciences and medical school. Biology has become data-intensive science. Bottleneck in being able to make sense of biological processes has shifted from data generation to statistical models and inference algorithms that can analyze these datasets. Statistical machine learning provides important toolkit in this endeavor. Biological datasets offer new challenges to field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions. Letter grading.
Background Courses in Data Science, Genomics, Computational Biology, and Other Related Areas (For Elective Choices When Available):
These courses are excellent opportunity to take in Data Science, Genomics, Computational Biology, and Other Related Areas for elective course purposes when available. They all satisfy electives in the Bioinformatics M.S program. Remember, these course are controlled by other departments on campus. Bioinformatics does not have access to these courses. There is no guarantee that you will be placed in the course. It is your responsibility to reach out to the instructor of record and/or main department SAO to see if you can take the course and receive a PTE Number to register, if the course is restricted due to major status.
COM SCI 245. Big Data Analytics
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Requisite: course 143 or 180 or equivalent. With unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is emerging economic and scientific need to extract useful information from it. Data analytics is process of automatic discovery of patterns, changes, associations, and anomalies in massive databases, and is highly inter-disciplinary field representing confluence of several disciplines, including database systems, data warehousing, data mining, machine learning, statistics, algorithms, data visualization, and cloud computing. Survey of main topics in big data analytics and latest advances, as well as wide spectrum of applications such as bioinformatics, E-commerce, environmental study, financial market study, multimedia data processing, network monitoring, social media analysis. Letter grading.
COM SCI 247. Advanced Data Mining
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Requisite: course 145 or M146 or equivalent. Introduction of concepts, algorithms, and techniques of data mining on different types of datasets, covering basic data mining algorithms, advanced topics on text mining, recommender systems, and graph/network mining. Team-based project involving hands-on practice of mining useful knowledge from large data sets is required. Letter grading.
COM SCI 260. Machine Learning Algorithms
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisite: course 180. Problems of identifying patterns in data. Machine learning allows computers to learn potentially complex patterns from data and to make decisions based on these patterns. Introduction to fundamentals of this discipline to provide both conceptual grounding and practical experience with several learning algorithms. Techniques and examples used in areas such as healthcare, financial systems, commerce, and social networking. Letter grading.
COM SCI 260C. Deep Learning
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: courses 180, 260. Not open to students with credit for Electrical and Computer Engineering C147 or C247. Study of basics of deep neural networks and their applications, including but not limited to computer vision, natural language processing, and graph mining. Covers topics including foundation of deep learning, how to train neural network (optimization), architecture designs for various tasks, and other advanced topics. By course end, students are expected to be familiar with deep learning and be able to apply deep learning algorithms to variety of tasks. Letter grading.
COM SCI 262A. Learning and Reasoning with Bayesian Networks
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Requisite: course 112 or Electrical Engineering 131A. Review of several formalisms for representing and managing uncertainty in reasoning systems; presentation of comprehensive description of Bayesian inference using belief networks representation. Letter grading.
COM SCI 263. Natural Language Processing
Units: 4
Lecture, four hours; discussion, two hours; outside study, six hours. Natural language processing (NLP) enables computers to understand and process human languages. NLP techniques have been widely used in many applications, including machine translation, question answering, machine summarization, and information extraction. Study of fundamental elements and recent trends in NLP. Students gain ability to apply NLP techniques in text-orientated applications, understand machine learning and algorithms used in NLP, and propose new approaches to solve NLP problems. Letter grading.
BIOMATH 210. Optimization Methods in Biology
Units: 4
Lecture, four hours. Preparation: undergraduate mathematical analysis and linear algebra; familiarity with programming language such as Fortran or C. Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics. S/U or letter grading.
STATS 201B. Statistical Modeling and Learning
Units: 4
Lecture, three hours; discussion, one hour. Requisites: courses 200A, 201A. Methods of model fitting and parameter estimation, with emphasis on regression and classification techniques, including those from machine learning. Interest in either obtaining suitable conditional expectation function or estimating meaningful parameters of underlying probabilistic model to make inferences or predictions from data. Focus on what is to be done when linear models are not appropriate and may produce misleading estimates. Coverage of classical must know model fitting and parameter estimation techniques such as maximum likelihood fitting of generalized linear models. Exploration of broader regression/classification techniques that have been ubiquitous in machine learning literature, with special attention to regularization and kernelized methods. S/U or letter grading.
STATS 205. Hierarchical Linear Models
Units: 4
Lecture, three hours. Designed for students in statistics and other disciplines who want to perform data analysis using linear and nonlinear regression and multilevel models. Introduction to and demonstration of wide variety of models to instruct students in how to fit these models using freely available software packages. Topics include regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding models provided. S/U or letter grading.
STATS C261. Introduction to Pattern Recognition and Machine Learning
Units: 4
Lecture, three hours. Requisites: course 100B, Mathematics 33A. Introduction to pattern analysis and machine intelligence designed for advanced undergraduate and graduate students. Concurrently scheduled with course C161. S/U or letter grading.
COM SCI 221. Probabilistic Models in Computational Genomics
Units: 4
(Same as Bioinformatics M221, Chemistry CM260A, and Human Genetics M260A.) Lecture, four hours; discussion, two hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Prior knowledge of biology not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms. Concurrently scheduled with course CM121. S/U or letter grading.
COM SCI 222. Algorithms in Computational Genomics
Units: 4
(Same as Bioinformatics M222 and Chemistry CM260B.) Lecture, four hours; discussion, two hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Course CM221 is not requisite to CM222. Designed for engineering students as well as students from biological sciences and medical school. Development and application of computational approaches to biological questions, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Computational techniques include those from statistics and computer science. Concurrently scheduled with course CM122. Letter grading.
COM SCI 224. Machine Learning Applications in Genetics
Units: 4
(Same as Bioinformatics M224 and Human Genetics CM224.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, Mathematics 33A, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Concurrently scheduled with course CM124. Letter grading.
COM SCI 225. Methods and Applications in Computational Genomics
Units: 4
(Same as Bioinformatics M225 and Human Genetics M265.) Lecture, two and one half hours; discussion, two and one half hours; outside study, seven hours. Introduction to computational approaches in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Topics include genome analysis, regulatory genomics, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genomic technologies. Computational techniques and methods include those from statistics and computer science. Letter grading.
COM SCI 226. Machine Learning in Computational Genomics
Units: 4
(Same as Computer Science M226 and Human Genetics M226.) Lecture, four hours; outside study, eight hours. Enforced requisite: Computer Science 32 or Program in Computing 10C with grade of C- or better. Recommended: one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Familiarity with probability, statistics, linear algebra, and algorithms expected. Designed for engineering students as well as students from biological sciences and medical school. Biology has become data-intensive science. Bottleneck in being able to make sense of biological processes has shifted from data generation to statistical models and inference algorithms that can analyze these datasets. Statistical machine learning provides important toolkit in this endeavor. Biological datasets offer new challenges to field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions. Letter grading.
COM SCI 228. Medical Decision Making
Units: 4
(Same as Bioengineering M228.) Lecture, four hours; outside study, eight hours. Designed for graduate students. Overview of issues related to medical decision making. Introduction to concept of evidence-based medicine and decision processes related to process of care and outcomes. Basic probability and statistics to understand research results and evaluations, and algorithmic methods for decision-making processes (Bayes theorem, decision trees). Study design, hypothesis testing, and estimation. Focus on technical advances in medical decision support systems and expert systems, with review of classic and current research. Introduction to common statistical and decision-making software packages to familiarize students with current tools. Letter grading.
COM SCI 286. Computational Systems Biology: Modeling and Simulation of Biological Systems
Units: 5
(Same as Bioengineering CM286.) Lecture, four hours; laboratory, two hours; discussion, one hour. Requisites: Life Sciences 30A, 30B, Mathematics 32A or M32T, 33A, and 33B; or Mathematics 31A, 31B, 32A or M32T, 33A, and 33B. Dynamic biosystem modeling and computer simulation methods for studying biological/biomedical processes and systems at multiple levels of organization. Intermediate linear and nonlinear control system, multicompartmental, epidemiological, pharmacokinetic, and other biomodeling methods applied to life sciences problems at molecular, cellular, organ, and population levels. Both theory- and data-driven modeling, with focus on translating biomodeling goals and data into dynamical mathematical models, and implementing them for simulation, quantification, and analysis. Numerical simulation, optimization, and parameter identifiability and search algorithms, with model discrimination and analysis and software exercises in PC laboratory assignments. Concurrently scheduled with course CM186. Letter grading.
BIOMATH 201. Deterministic Models in Biology
Units: 4
Lecture, three hours; laboratory, three hours. Preparation: knowledge of linear algebra and differential equations. Examination of conditions under which deterministic approaches can be employed and conditions where they may be expected to fail. Topics include compartmental analysis, enzyme kinetics, physiological control systems, and cellular/animal population models. S/U or letter grading.
BIOMATH M203. Stochastic Models in Biology
Units: 4
(Same as Human Genetics M203.) Lecture, four hours. Requisite: Mathematics 170A or equivalent experience in probability. Mathematical description of biological relationships, with particular attention to areas where conditions for deterministic models are inadequate. Examples of stochastic models from genetics, physiology, ecology, and variety of other biological and medical disciplines. S/U or letter grading.
BIOMATH 205. Top Computational Algorithms
Units: 4
Lecture, four hours. Overview of the most important and beautiful algorithms in numerical analysis, statistics, bioinformatics, and computer science. Emphasis will be on mathematical derivation, practical complexity analysis, significant applications, and coding in the Julia programming language. Big data applications particularly stressed. Letter grading only.
BIOMATH M207A. Theoretical Genetic Modeling
Units: 4
(Same as Biostatistics M272 and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading.
BIOMATH M207B. Applied Genetic Modeling
Units: 4
(Same as Biostatistics M237 and Human Genetics M207B.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B. Methods of computer-oriented human genetic analysis. Topics include statistical methodology underlying genetic analysis of both quantitative and qualitative complex traits. Laboratory for hands-on computer analysis of genetic data; laboratory reports required. Course complements M207A; students may take either and are encouraged to take both. S/U or letter grading.
BIOMATH M211. Mathematical and Statistical Phylogenetics
Units: 4
(Same as Biostatistics M239 and Human Genetics M211.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B, Mathematics 170A. Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data. S/U or letter grading.
BIOL CH 251A. Seminar: Transcriptional Regulation
Units: 2
Seminar, two hours. Advanced course on mechanics of gene transcription in both eukaryotes and prokaryotes intended for students actively working or highly interested in transcription. S/U grading.]
BIOL CH 251B. Seminar: Transcriptional Regulation
Units: 2
Seminar, two hours. Advanced course on mechanics of gene transcription in both eukaryotes and prokaryotes intended for students actively working or highly interested in transcription. S/U grading.
CHEM C200. Genomics and Computational Biology
Units: 5
Lecture, four hours; discussion, one hour. Introduction for biochemistry students of technologies and experimental data of genomics, as well as computational tools for analyzing them. Biochemistry and molecular biology dissected life into its component parts, one gene at time, but lacked integrative mechanisms for putting this information back together to predict what happens in complete organism (e.g., over 80 percent of drug candidates fail in clinical trials). High-throughput technologies such as sequencing, microarrays, mass-spec, and robotics have given biologists incredible new capabilities to analyze complete genomes, expression patterns, functions, and interactions across whole organisms, populations, and species. Use and analysis of such datasets becomes essential daily activity for biomedical scientists. Core principles and methodologies for analyzing genomics data to answer biological and medical questions, with focus on concepts that guide data analysis rather than algorithm details. Concurrently scheduled with course C100. S/U or letter grading.
CHEM 256N. Seminar: Research in Biochemistry — Advanced Topics in Structural Biology
Units: 2
Seminar, three hours. Advanced study and analysis of current topics in biochemistry. Discussion of current research and literature in research specialty of faculty member teaching course. S/U grading.
CHEM C265. Metabolic Control by Protein Modification
Units: 4
Lecture, three hours; discussion, one hour. Requisites: courses 153A, 153B, 153C. Biochemical basis of controlling metabolic pathways by posttranslational modification of proteins, including phosphorylation and methylation reactions. Concurrently scheduled with course C165. Letter grading.
CHEM 266. Proteomics and Protein Mass Spectrometry
Units: 3
Lecture, two hours. Essential technologies and concepts practiced in proteomics-based research, including methods for protein separation and display, protein quantitation, and protein identification. Emphasis on fundamentals of protein mass spectrometry. S/U or letter grading.
EE BIOL M200A. Evolutionary Biology
Units: 4
(Same as Earth, Planetary, and Space Sciences M216.) Lecture, two hours; discussion, two hours. Current concepts and topics in evolutionary biology, including microevolution, speciation and species concepts, analytical biogeography, adaptive radiation, mass extinction, community evolution, molecular evolution, and development of evolutionary thought. S/U or letter grading.
EE BIOL C235. Population Genetics
Units: 4
(Formerly numbered 235.) Lecture, three hours; discussion, one hour. Basic principles of genetics of population, dealing with genetic structure of natural populations and mechanisms of evolution. Equilibrium conditions and forces altering gene frequencies, polygenic inheritance, molecular evolution, and methods of quantitative genetics. Concurrently scheduled with course C135. S/U or letter grading.
EE BIOL 263. Seminar: Population Genetics
Units: 2 or 4
Seminar, three to six hours. Seminar on topics of current interest in population genetics, such as kin selection, sociobiology, cultural evolution, conservation genetics, etc. S/U or letter grading.
EE BIOL 297. Selected Topics in Ecology and Evolutionary Biology
Units: 1 to 4
Seminar, one to three hours. Advanced study and analysis of variable research topics in research issues in ecology and evolutionary biology. Consult “Schedule of Classes” for topics and instructors. May be repeated for credit with consent of instructor. S/U or letter grading.
HUM GEN 210. Topics in Genomics
Units: 2
Seminar, two hours. Survey of current biological theory and technology used in genomic research. Topics include genomic technologies, functional genomics, proteomics, statistical genetics, bioinformatics, and ethical issues in human genetics. S/U grading.
HUM GEN 236A. Advanced Human Genetics A: Molecular Aspects
Units: 4
Lecture, three hours. Recommended preparation: prior knowledge of basic concepts in molecular biology and genetics. Advanced topics in human genetics related to molecular genetics and relevant technologies. Topics include genomic technologies, human genome, mapping and identification of disease-causing mutations, transcriptomics, proteomics, functional genomics, epigenetics, and stem cells. Reading materials include original research articles and reviews or book chapters. Letter grading.
HUM GEN 236B. Advanced Human Genetics B: Statistical Aspects
Units: 4
Lecture, three hours; computer laboratory, one hour. Recommended preparation: introductory statistics knowledge equivalent to Biostatistics 100A or Statistics 13 and general genetics knowledge equivalent to Ecology and Evolutionary Biology 121, Human Genetics 236A, or Molecular, Cell, and Developmental Biology 144. Statistical and population genetics related to analysis of complex human genetic traits. Reading materials include original research papers and reviews. Letter grading.
HUM GEN C244. Genomic Technology
Units: 4
Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Survey of key technologies that have led to successful application of genomics to biology, with focus on theory behind specific genome-wide technologies and their current applications. Concurrently scheduled with course C144. S/U or letter grading.
HUM GEN M255. Mapping and Mining Human Genome
Units: 3
(Same as Pathology M255.) Lecture, three hours. Basic molecular genetic and cytogenetic techniques of gene mapping. Selected regions of human genomic map scrutinized in detail, particularly gene families and clusters of genes that have remained linked from mouse to human. Discussion of localizations of disease genes. S/U or letter grading.
BIOSTAT 202A. Theoretical Principles of Biostatistics
Units: 4
Lecture, three hours; discussion, one hour. Recommended preparation: two years of calculus and linear algebra. Introduction to main principles of probability, random variables, discrete and continuous distributions, bivariate distributions, and distributions of functions of random variables. Letter grading.
BIOSTAT 202B. Topics in Estimation
Units: 4
Lecture, three hours; discussion, one hour. Requisite: course 202A. Basic concepts, sufficiency, biasedness, approximation methods in statistics, nonparametric models and estimation methods, maximum likelihood estimation, M-estimation, Bayesian estimation, and hypotheses testing. Letter grading.
BIOSTAT 276. Inferential Techniques that Use Simulation
Units: 4
Lecture, three hours; discussion, one hour. Requisites: Statistics 200A, 200B. Recommended: course 213. Theory and application of recently developed techniques for statistical inference that use computer simulation. Topics include bootstrap, multiple imputation, data augmentation, stochastic relaxation, and sampling/importance resampling algorithm. S/U or letter grading.
COM SCI 262A. Learning and Reasoning with Bayesian Networks
Units: 4
Lecture, four hours; outside study, eight hours. Requisite: course 112 or Electrical Engineering 131A. Review of several formalisms for representing and managing uncertainty in reasoning systems; presentation of comprehensive description of Bayesian inference using belief networks representation. Letter grading.
EL ENGR 236A. Linear Programming
Units: 4
Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: Mathematics 115A or equivalent knowledge of linear algebra. Basic graduate course in linear optimization. Geometry of linear programming. Duality. Simplex method. Interior-point methods. Decomposition and large-scale linear programming. Quadratic programming and complementary pivot theory. Engineering applications. Introduction to integer linear programming and computational complexity theory. Letter grading.
EL ENGR 236B. Convex Optimization
Units: 4
Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: course 236A. Introduction to convex optimization and its applications. Convex sets, functions, and basics of convex analysis. Convex optimization problems (linear and quadratic programming, second-order cone and semidefinite programming, geometric programming). Lagrange duality and optimality conditions. Applications of convex optimization. Unconstrained minimization methods. Interior-point and cutting-plane algorithms. Introduction to nonlinear programming. Letter grading.
EL ENGR 236C. Optimization Methods for Large-Scale Systems
Units: 4
Lecture, four hours; outside study, eight hours. Requisite: course 236B. First-order algorithms for convex optimization: subgradient method, conjugate gradient method, proximal gradient and accelerated proximal gradient methods, block coordinate descent. Decomposition of large-scale optimization problems. Augmented Lagrangian method and alternating direction method of multipliers. Monotone operators and operator-splitting algorithms. Second-order algorithms: inexact Newton methods, interior-point algorithms for conic optimization. Letter grading.
M PHARMA 287. Business of Science
Units: 2
Lecture, two hours. Designed for graduate students. (undergraduate students may enroll with consent of instructor). Introduction to principles of business and entrepreneurship in technology sectors. Basic business skills taught to effectively perform in commercial environment and within academic environment. Application of course material by performing feasibility studies that have potential to receive funding and become actual companies. Exploration of entrepreneurship, particularly formation and operation of new business ventures. Presentations by and questioning of successful technology entrepreneurs, identifying and evaluating new venture opportunities, development of financing, and entry and exit strategies. S/U or letter grading.
MIMG C222. Mouse Molecular Genetics
Units: 2
(Formerly numbered CM222.) Seminar, two hours. Enforced requisite: Life Sciences 4. Designed for students doing research with mice. During past 25 years, molecular revolution has greatly increased power and scope of mouse genetics, and today mouse is primary experimental model in virtually all fields of biology and biomedicine. Seminar forum for in-depth discussion of tools and technologies of mouse genetics and their application to functional genomics, complex traits, stem cell biology, developmental biology, epigenetics, and genetic dissection of diseases. Concurrently scheduled with course C122. S/U or letter grading.
PATH M272. Stem Cell Biology and Regenerative Medicine
Units: 4
(Same as Molecular, Cell, and Developmental Biology M272.) Lecture, two hours; discussion, two hours. Designed for graduate students. Presentation of current knowledge of embryonic and adult stem cells and factors that regulate their growth and development. Major emphasis on how advances in cell and molecular biology and tissue engineering can be applied to use of stem cells in regenerative medicine. Bioethical and legal issues related to stem cell research. S/U or letter grading.
PHYSCI M200. Advanced Experimental Statistics
Units: 4
(Formerly numbered M200.) Lecture, four hours; laboratory, one hour. Introduction to statistics with focus on computer simulation instead of formulas. Bootstrap and Monte Carlo methods used to analyze physiological data. S/U or letter grading.
STATS 200A. Applied Probability
Units: 4
Lecture, three hours. Requisite: course 100A or Mathematics 170A. Limited to graduate statistics students. Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology. S/U or letter grading.
STATS 200B. Theoretical Statistics
Units: 4
Lecture, three hours. Sufficiency, exponential families, least squares, maximum likelihood estimation, Bayesian estimation, Fisher information, Cramér/Rao inequality, Stein’s estimate, empirical Bayes, shrinkage and penalty, confidence intervals. Likelihood ratio test, p-value, false discovery, nonparametrics, semi-parametrics, model selection, dimension reduction. S/U or letter grading.
STATS M231. Pattern Recognition and Machine Learning
Units: 4
(Same as Computer Science M276A.) Lecture, three hours. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.
STATS C236. Introduction to Bayesian Statistics
Units: 4
Lecture, three hours; discussion, one hour. Recommended requisite: course 200A or 200B. Designed for graduate students. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning. Examples of applications vary according to interests of students. Concurrently scheduled with course C180. S/U or letter grading.
STATS C261. Introduction to Pattern Recognition and Machine Learning
Units: 4
Lecture, three hours. Requisites: course 100B, Mathematics 33A. Introduction to pattern analysis and machine intelligence designed for advanced undergraduate and graduate students. Concurrently scheduled with course C161. S/U or letter grading.
BIOSTAT M235 – Causal Inference
Units: 4
(Same as Psychiatry M232.) Lecture, three hours; discussion, one hour. Requisite: course 200A. Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman. Rubin model for causal inference, propensity scores. Analysis of clinical trials with noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural equation, and graphical models. Decision making when causality is disputed. Letter grading.
BIOMATH 204 – Biomedical Data Analysis
Units: 4
Lecture, four hours. Quantity and quality of observations have been greatly affected by present-day extensive use of computers. Problem-oriented study of latest methods in statistical data analysis and use of such arising in laboratory and clinical research. S/U or letter grading.
BIOENGR M226- Medical Knowledge Representation
Unit: 4
Description: (Same as Information Studies M253.) Seminar, four hours; outside study, eight hours. Designed for graduate students. Issues related to medical knowledge representation and its application in healthcare processes. Topics include data structures used for representing knowledge (conceptual graphs, frame-based models), different data models for representing spatio-temporal information, rule-based implementations, current statistical methods for discovery of knowledge (data mining, statistical classifiers, and hierarchical classification), and basic information retrieval. Review of work in constructing ontologies, with focus on problems in implementation and definition. Common medical ontologies, coding schemes, and standardized indices/terminologies (SNOMED, UMLS). Letter grading.
COM SCI 275-Artificial Life for Computer Graphics and Vision
Unit: 4
Lecture, four hours; outside study, eight hours. Enforced requisite: course 174A. Recommended: course 161. Investigation of important role that concepts from artificial life, emerging discipline that spans computational and biological sciences, can play in construction of advanced computer graphics and vision models for virtual reality, animation, interactive games, active vision, visual sensor networks, medical image analysis, etc. Focus on comprehensive models that can realistically emulate variety of living things (plants and animals) from lower animals to humans. Exposure to effective computational modeling of natural phenomena of life and their incorporation into sophisticated, self-animating graphical entities. Specific topics include modeling plants using L-systems, biomechanical simulation and control, behavioral animation, reinforcement and neural-network learning of locomotion, cognitive modeling, artificial animals and humans, human facial animation, and artificial evolution. Letter grading.
STATS 203-Large Sample Theory, Including Resampling
Unit: 4
(Formerly numbered 200C.) Lecture, three hours. Requisite: course 200B. Asymptotic properties of tests and estimates, consistency and efficiency, likelihood ratio tests, chi-squared tests. S/U or letter grading.
NEURO 240 – Phenotypic Measurement of Complex Traits
Unit: 4
Lecture, three hours. Preparation: background in human genetics helpful. Integrative approach to understanding gene to behavior pathways by examination of levels of phenotype expression across systems (cell, brain, organism), across species (invertebrate, fly, mouse, human), and throughout development across varying environmental milieus. Using examples from human disorders such as schizophrenia and Alzheimer’s disease, linking of these diverse approaches in genetic research to map out integrative system of understanding basis of complex human behavior. Emphasis on basic understanding of methods used at each level of phenotype analysis, along with major resources that can be accessed to gain insight to gene-behavioral links. Letter grading.
STATS M243-Logic, Causation, and Probability
Unit: 4
(Same as Epidemiology M204.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: Epidemiology 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.
STATS 232C – Cognitive Artificial Intelligence
Unit: 4
Lecture, three hours. Recommended requisites: courses M232A, M232B. Demonstration of how to build artificial intelligence by following principles of human intelligence revealed by cognitive science, including learning from small data, expressing causality of physical world, and inferring mental states of others for intuitive social interactions. Draws from statistical modeling, cognitive science, artificial intelligence, computer vision, and robotics. S/U or letter grading.
EC ENGR 239AS-Special Topics in Signals and Systems
Unit: 4
(Formerly numbered Electrical Engineering 239AS.) Lecture, four hours; discussion, one hour; outside study, seven hours. Special topics in one or more aspects of signals and systems, such as communications, control, image processing, information theory, multimedia, computer networking, optimization, speech processing, telecommunications, and VLSI signal processing. May be repeated for credit with topic change. S/U or letter grading.
NEURBIO M200B – Cell, Developmental, and Molecular Neurobiology
Unit: 6
(Same as Neuroscience M201.) Lecture, six hours. Fundamental topics concerning cellular, developmental, and molecular neurobiology, including intracellular signaling, cell-cell communication, neurogenesis and migration, synapse formation and elimination, programmed neuronal death, and neurotropic factors. Letter grading.
BIOSTAT 203B – Introduction to Data Science
Unit: 4
Lecture, three hours; laboratory, two hours. Requisite: course 203A. Principles of data science. Topics include Health Insurance Portability and Accountability Act (HIPAA) and data ethics, databases and data retrieval, data merging and cleaning, data visualization and web presentation, reproducible research, collaborative research, cluster computing, and cloud computing. S/U or letter grading.