fi2151 2024-1

Materials, in the form of slide, given in FI2151 Modeling, Data Generator and Analysis course in 2024-1 semester.

Materials:

• Software and platform installation
  Intro, Python installation, Virtual environment #1, Virtual environment #2, Clone a vitual environment, Closing.
• Python basics and more
  Intro with hello world, GitHub gist, Python list and NumPy array.
• Python list
  Characteristics and methods, Create list, Display lists elements, Check type, Change elements, Convert others to list, Change elements order, Analyze elements, List comprehension, For llop, Function returning list.
• NumPy array and observation
  Intro, Initialize with integer, Initialize with float, Durating and sampling rate, Closing.
• Folder-based Datalake for Student
  Intro, Available data (as examples), Access all data, Closing.
• Intro to Matlotlibline plot
  Intro, Minimum code and list, Grid and axis features, Multiple series, User defined function, Legend, Linewidth and RGB color, Lissajous curve, Closing.
• Intro to Matplotlib scatter plot
  Intro, Minimum code with list, Features of axis and grid, Multiple series, Legend, Marker customization.
• Unlimited visualization data link
  …
• Take advantage of data lake strength
  Intro, Available data (as examples), Access all data, Futher exploration with Jupyter Notebook, Closing.
• Data lake with Python and Jupyter Notebook )
  Intro, Available data (as examples), Access all data, Futher exploration with Jupyter Notebook, Admission case, Calculate applicant score, Merge applicant batch, Collect semester data, Calcula semester selectivity, Create graphics, Create report, Closing.
• Journal introduction with AI
  Intro, Academic writing, Peer review, Policy, Log in to ChatGPT, Ask a question, Ask for references, Create paragraphs with terms, Closing.
• Intro to pandas DataFrame
  Intro, Create DataFrame, Access as list, Access as DataFrame, Change index, Access a cell, Save DataFrame to XLSX file, Read file XLSX and CVS, Assignments.
• Descriptive statistics: An intro
  Definition, Objectives, Central tendency, Variability, Relationship, Practicing centra tendency, Dicrete uniform distribution, Correlation, Normal distribution, Correlation two distributions.
• Brief intro to random number
  Intro, Random module, random(), seed(), randint(), randrange(), state, getrandsbits(), choice() and choices(), sample(), sample() vs choices(), shuffle(), Random distribution, Closing.
• Modeling, Data Generator, n Analysis
  Intro, Materials, Assignments, Midtermm.
• Random walk intro
  Intro, Some systems, ABM examples, Codes, Closing.
• Random walk displacement Intro Paths, Distance, Displacement, Mean absolute displacement, Mean squared displacement, Repetition, Closing.
• Short intro to model
  Intro, Physical model, Collection of physical models, Symbolical model, Empirical model, Mathematical model, Computational model, Interpolation, Accuracy and simplicity, Closing.
• FI2151 2024-1 survey discussion
  Intro, Quantitative feedback, Qualitative feedback, Closing.
• Data Science and Python venv
  Data science, Some intro to machine learning, Python virtual environment, Steps to create virtual environment, Python statistics module, Dicussion, Assigment.
• Neural network intro
  Intro, Biological neuron, Artificial neuron, Neural networks, The zoo, Challenges.
• ANN with spreadsheet
  Intro, Draw NN architecture schematics, ANN simple example, Output activation modification, Closing.
• Intro to perceptron
  Intro, Dataset generation, Produced datasets, Logical gates, AI-assisted info, Closing.
• Further intro to perceptron
  Intro, Linear separable dataset generation, Feed forward in single-layer perceptron, Binary classification ability, Decision boundary line, Learning process, Error estimation, Additional topic as intermezzo, Closing.
• ML synthetic data with Python
  Intro, Synthetic data creation, Libraries, packages, load data, Data visualization, Build and test models, Make predictions, Omitting X3 column, Closing.
• TensorFlow Playground1
  Intro, The website, Datasets, Setting, Some terms, Models, Closing.
• Multidimensional visualization
  Intro, .., Closing.