Topics will be announced when offered.

This advanced course will help students to understand fundamental methods used for materials characterization. Students will learn principles and applications of detectors and amplifiers, optical spectroscopy, electron and scanning probe microscopy, X-ray diffraction, fluorescence and spectroscopic methods, surface analysis techniques. Students will be able to use the knowledge in the broad area of materials research. By the end of the course, the students will be able to choose appropriate methods for characterizing each specific type of materials and to treat and analyze the data obtained by such techniques.

Fabrication and characterization techniques for micro and nano electro mechanical systems, MEMS & NEMS (including: microlithography; wet & dry etching techniques; physical & chemical vapor deposition processes; electroplating; bonding; focused ion beams; top-down approaches - electron-beam lithography, SPM, soft lithography - ; bottom-up techniques based on self-assembly). Semiconductor nanotechnology. Nanotubes & nanowires. Biological systems. Molecular electronics.

A series of lectures given by faculty or outside speakers. Participating students must also make presentations during the semester.

Linear algebra and matrix theory; mathematics of finance; counting and the fundamentals of probability theory; game theory.

Linear algebra and matrix theory; mathematics of finance; counting and the fundamentals of probability theory; game theory.

Linear algebra and matrix theory; mathematics of finance; counting and the fundamentals of probability theory; game theory.

Linear algebra and matrix theory; mathematics of finance; counting and the fundamentals of probability theory; game theory.

Linear algebra and matrix theory; mathematics of finance; counting and the fundamentals of probability theory; game theory.

Limit of a function; Continuous functions and their properties; Derivative andapplications; Extreme values; Indefinite integral; Riemann integral and fundamental theorem of calculus; Logarithmic and exponential functions; L?Hospital?s rule; Sequence and series of numbers; Power series and their properties;

Limit of a function; Continuous functions and their properties; Derivative andapplications; Extreme values; Indefinite integral; Riemann integral and fundamental theorem of calculus; Logarithmic and exponential functions; L?Hospital?s rule; Sequence and series of numbers; Power series and their properties;

Sets; logic and implications; proof techniques with examples; mathematical induction and well-ordering; equivalence relations; functions; cardinality; countable and uncountable sets.

Limits and continuity; derivative and properties of differentiable functions; mean value theorems, Taylor's formula, extreme values; indefinite integral and integral rules; Riemann integral and fundamental theorem of calculus; L'Hospital's rule; improper integrals.

Limits and continuity; derivative and properties of differentiable functions; mean value theorems, Taylor's formula, extreme values; indefinite integral and integral rules; Riemann integral and fundamental theorem of calculus; L'Hospital's rule; improper integrals.

Limits and continuity; derivative and properties of differentiable functions; mean value theorems, Taylor's formula, extreme values; indefinite integral and integral rules; Riemann integral and fundamental theorem of calculus; L'Hospital's rule; improper integrals.

Limits and continuity; derivative and properties of differentiable functions; mean value theorems, Taylor's formula, extreme values; indefinite integral and integral rules; Riemann integral and fundamental theorem of calculus; L'Hospital's rule; improper integrals.

Vectors; matrices and systems of linear equations; vector spaces; linear maps; orthogonality; algebra of complex numbers; eigenvalue problems.

Vectors; matrices and systems of linear equations; vector spaces; linear maps; orthogonality; algebra of complex numbers; eigenvalue problems.

Descriptive statistics; measures of association, correlation, simple regression; probability theory, conditional probability, independence; random variables and probability distributions; sampling distributions; estimation; inference (confidence intervals and hypothesis testing). Topics are supported by computer applications.

Descriptive statistics; measures of association, correlation, simple regression; probability theory, conditional probability, independence; random variables and probability distributions; sampling distributions; estimation; inference (confidence intervals and hypothesis testing). Topics are supported by computer applications.

Descriptive statistics; measures of association, correlation, simple regression; probability theory, conditional probability, independence; random variables and probability distributions; sampling distributions; estimation; inference (confidence intervals and hypothesis testing). Topics are supported by computer applications.

Descriptive statistics; measures of association, correlation, simple regression; probability theory, conditional probability, independence; random variables and probability distributions; sampling distributions; estimation; inference (confidence intervals and hypothesis testing). Topics are supported by computer applications.

A course in basic concepts and tools of statistics for students who will study social and Behavioral sciences. Topics to be covered are representation of quantitative information in social sciences, forms of numerical data, creating and interpreting graphical and tabular summaries of data, descriptive statistics, estimation of population parameters, confidence intervals, basic hypothesis testing, t-statistics, chi-squared tests and analysis of variance.

Functions of several variables; partial differentiation; directional derivatives; exact differentials; multiple integrals and their applications; vector analysis; line and surface integrals; Green?s, Divergence and Stoke?s theorems.