This is a repository for all the code I write for classes (notes, homeworks, projects, etc.).
Hands-on exposure to the following major topics: Problem solving, algorithm design and development, structure of the program, top-down design and functional decomposition, debugging, elementary data types, expressions, I/O functions and formats, repetition and control structures, user-defined functions, pass by value, pass by reference, built-in functions, arrays, strings.
Hands-on exposure to major topics in data structures and control, including file I/O; abstract data types; static and dynamic data structures; pointers and pointer operations; templates, memory addresses; garbage collection; memory leak; function and operator overloading; constructors and destructors; deep and shallow copying; class concepts; multi-dimensional and dynamic arrays; linked lists; doubly-linked lists; stacks, queues and their implementations and applications. The course provides a computer laboratory component to ensure practice with the above concepts.
Introduction to the basic concepts of computer organization, digital logic, data representation, and machine instructions repertoire; memory access and storage; instruction execution; assembly language; computer organization; levels of computer structures; data representation and transfer; digital arithmetic; memory structure and addressing methods; cache; secondary memory structure and organization.
Review of basic data structures and algorithmic complexities; recursion; topological order; Sorting and searching; Huffman codes; tries; binary trees; binary search trees; tree traversals; general trees, heaps, balanced trees; priority queues; hashing; graphs, graph algorithms.
Introduction to digital image and signal processing, computer vision and pattern recognition; image acquisition, registry and display; elementary image processing algorithms: sampling, preprocessing, smoothing, segmentation, and sharpening; transformations; filtering; image coding and restoration; analog and digital images and image processing systems; feature extraction and selection; elementary pattern classification and vision systems; robotics; machine learning.
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Vector-tensor approach to classical mechanics including kinematics, dynamics, oscillations, Lagrange's and Hamilton's equations, transformations, central force, and rigid body motion.
Simple linear regression and multiple regression including inference, diagnostics and transformations. One-way and multi-way analysis of variance including inference, diagnostics and transformations. Use of professional statistical software.
A continuation of STAT 260: statistical foundations of data science; bootstrap methods; supervised learning; unsupervised learning; simulation; interactive data graphics; working with spatial data and text; working with large data sets.