Also cool to look at:

• 234124-MatamFinalProject - Graphs calculator
• 236756-MachineLearningHW2 - K-nearest neighbors, decision trees, SVM using SGD and feature mapping
• 236756-MachineLearningHW3 - Linear regression implementation, evaluation and baseline, Ridge linear regression, lasso linear regression and polynomial fitting
• 236523-FinalProjectInBioinformatics - Gene expression analysis

• 234114 - Introduction to Computer Science (IntroToCS)

  • 234114-IntroToCSHW3 - Avoidance Tic-Tac-Toe

• 234124 Introduction to Systems Programming (Matam)

  • 234124-MatamHW1 - Map (Abstract data type implementation) , Election system implementation
  • 234124-MatamHW2 - Building python interface for C module using SWIG
  • 234124-MatamHW3 - Generics, operator overloading, exceptions, polymorphism
  • 234124-MatamFinalProject - Graphs calculator - Too vast to fit in a sentence:)

• 236319 Programming Languages

  • 236319-ProgrammingLanguagesHW1
  • 236319-ProgrammingLanguagesHW2
  • 236319-ProgrammingLanguagesHW3
  • 236319-ProgrammingLanguagesHW4
  • 236319-ProgrammingLanguagesHW5
  • 236319-ProgrammingLanguagesHW6

• 234123 - Operating Systems

  • 234123-OperatingSystemsHW1 - Shell, jobs list, signals
  • 234123-OperatingSystemsHW2 - Adding a system call
  • 234123-OperatingSystemsHW3 - Multi-threaded server using condition variables
  • 234123-OperatingSystemsHW4 - Memory allocation (programming malloc)

• 234118 - Computer Organization And Programming (Atam)

  • 234118-ATAM_HW1 - Assembly basic questions
  • 234118-ATAM_HW2 - Assmebly matrix mutilation, interrupts
  • 234118-ATAM_HW3 - Hacking readelf, Linker scripts
  • 234118-ATAM_HW4 - Building a debugger

• 236523 - Introduction to Bioinformatics

  • 236523-IntroToBioInformaticsHW1
  • 236523-IntroToBioInformaticsHW2
  • 236523-IntroToBioInformaticsHW3
  • 236523-IntroToBioInformaticsHW4
  • 236523-FinalProject

• 236332 - IoT project

  • IoT project - gait analysis

• 236756 - Machine learning

  • HW1 - Data preparation: data imputation and cleaning, outlier detection, univariate feature exploration, missing data, feature selection

  • HW2 - Includes 3 parts:

    Part 1: k-nearest neighbors model implementation and selection, data normalization, model selection error anaylsis.

    Part 2: decision trees (model visualization and selection)

    Part 3: SVM implementation and optimization, solving SVM problems using stochastic gradient descent SGD, applying a feature mapping

  • HW3 - Linear regression implementation, evaluation and baseline, Ridge linear regression, lasso linear regression, polynomial fitting, testing the models

• 236781 - Deep learning

  • HW1
  • HW2
  • HW3
  • HW4