The course covers advanced theory and methods in machine learning, including probabilistic modeling, hypothesis testing, classification and regression,... maximum likelihood and Bayesian inference, PAC learning and VC theory, nonparametric methods, and state-of-the-art machine learning algorithms.
The course provides an introduction to the theory and practical methods for machine learning, and ... is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics, and algorithms of machine learning. Topics covered include nearest neighbor method, decision tree learning, Support Vector Machines, Bayesian networks, neural networks, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, mistake bounds, etc.
The course focuses on principles of knowledge-based search techniques, automatic deduction, knowledge... representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
This course is an introduction to machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis. Mathematical topics ...covered include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms.
The course introduces stages of a data mining project, organizational data audits, metadata and data management... concepts, data preparation techniques, data project evaluation and principles of data ethics. Students learn and apply introductory data mining tools and techniques for data clustering, dividing data into classes, making predictions, and identifying networks.
The main focus of this course lies on techniques for constructing correct efficient algorithms and on tools... to reason about them. Design paradigms include greed, divide-and-conquer, dynamic programming, reductions, and randomness (time permitting). A second focus point is computational intractability. NP-complete problems are covered, as well as ways to deal with them.
The course introduces balanced search trees, graphs, graph traversal algorithms, hash tables and sets, and... complexity analysis and about classes of problems that require each data type.
This is a Java programming course covers principles and practices of Object Oriented (OO) programming, analysis and ...design, and fundamental concepts in advanced data structures.
The course focuses on formulating and investigating novel questions with tools from data mining and learning analytics including social network analysis, natural language processing, Markov modeling, Bayesian inference, and agent-based modeling.
The course covers: principles of the visual presentation of data; survey information visualization, scientific visualization, and ... visual analytics; design and evaluation of visualizations and interactive exploration tools; introduction to relevant foundations in visual design, human perception, and data analysis; encodings, layout and interaction; approaches to large data sets; visualization of complex data types such as scalar fields, graphs, sets, texts, and multi-variate data.
The course covers: analysis of variance and covariance, multiple linear regression; chi-square and various nonparametric techniques.
The course covers: introductory descriptive statistics and statistical inference; measures of central tendency and variability, confidence intervals, theory of hypothesis testing, correlation techniques.
The objective of the course is to give students a broad overview of the various aspects of data analytics such as accessing,... cleansing, modeling, visualizing, and interpreting data. It includes hands-on training in Python, R, and other open-source analytic tools.
This course covers discussions of probability, data sampling, data summarization, sampling distributions, statistical inference, ...statistical pattern analysis, hypothesis testing, regression, and nonparametric inference over multidimensional data collections.
The course covers: information gathering methods, qualitative, quantitative, and mixed methods approaches, and various Human Factors Engineering methods for task/process/system description and analysis.
The primary goal of this course is to teach algorithms for problems such as: modeling sequence classes and features, multiomics analysis, gene discovery, ... network biology, applied machine learning, and single-cell genomic analysis. This class provides students with a strong background for conducting their own bioinformatics research.
The primary goal of the course was to teach algorithms for analyzing genomes, RNA, proteins, and ...biological networks. These techniques will provide students a strong background for conducting their own bioinformatics research.
This course intoduces fundamental concepts and basic computational techniques for mainstream bioinformatics problems. Emphasis... placed on computational aspect of bioinformatics including formulation of a biological problem, design of algorithms, confidence assessment of software development.
This course introduces recent advances in bioinformatics methods and software tools used in genomic research and its application. This... course covers mapping the genomic coordination to gene location, identifying similarities between sequences of different organisms, detecting mutations in diseases and pathological conditions. This course is designed for students from biology and non-biology backgrounds to learn the current genomics skillsets.
This course examines clinical, research, and administrative applications of information systems in health services delivery. Provides... an introduction to important topics in biomedical informatics, including clinical data (collection, storage, management), electronic medical record systems, decision support systems, computerized order entry, telemedicine, and consumer applications.
The course covered the latest trends in modern data management and machine learning systems designs to better support the next generation of ML applications, and applications of ML to optimize ...the architecture and the performance of data management systems.
This course provides an in-depth treatment of the evolution of high-performance, parallel computing architectures and how these architectures and computational ecosystems support data science. It ...covers topics such as parallel algorithms for numerical processing, parallel data search, and other parallel computing algorithms which facilitate advanced analytics.
The course covers a number of advanced topics in the development of database management systems (DBMS) and the modern applications of databases. The topics discussed include advanced ...concurrency control and recovery, query processing and optimization, advanced access methods, parallel and distributed data systems, extensible data systems, implications of cloud computing for data platforms, and data analysis on large datasets.
The course introduces the concept of a data model, the entity-relationship (ER) model, the relational model, and learn how to use the SQL query language. The logical and physical database design issues ...were also covered. The other half of the course concentrates on DBMS implementation. The course covers file organization, various indexing methods, techniques for external sorting, and how a DBMS implements a relational operator, and the basics of query optimization.