Schedule

• June

• July

• h = holiday
• • = Extra session
• – = Workshop in Singapore

Study plan

Code Assessment

• Ignite Code Presentation: 5-minute with 20 slides that automatically advance class explaining key aspects of the program:
  • Describe what the program was designed to do
  • Decsribe the algorithm and key implementation choices
  • Highlight of the software
  • Describe any novel approaches taken
  • Sample input and output
  • Current limitations of the software
• Provide a copy of the Project Jupyter file A dynamic research notebook to provide the background information for a research paper
  • Introduction: Capture the rationale, key goals and objectives of the project
  • Methodology: Descriptions and citations about the data sources and methodologies used, research steps and working code broken into documented fragments
    
  • Results: Comparison of research outcomes to expectation
  • Discussion: Intepretation of the results and possible future research directions

Red Flags for AI-Generated Code

Automatic point deductions may apply for:

• Code that student cannot explain or modify
• Sophisticated techniques not covered in class without explanation
• Inconsistent coding style within the same assignment
• Comments that don’t match the actual code
• Over-engineering for simple problems
• Generic variable names throughout (e.g., data, result, output)
• Perfect code with no evidence of iteration or debugging
• Ignite presentation that goes over 5min or fails to cover the essentials

Ideal Software

Note: Suspected unoriginal work may be subject to academic integrity procedures outlined in the course syllabus.

Key Disciplines

• Data Mining: The process of discovering patterns, insights, and anomalies from large datasets. The information extracted can be used for dataset development, decision-making, prediction, and/or understanding.

• Data Science: An interdisciplinary field that combines statistical methods, data manipulation and analysis and domain expertise to extract knowledge and insights from data. Data scientists are involved in the entire data lifecycle, from collection and cleaning to analysis, modeling, and communication of results.

• Artificial Intelligence (AI): A broad field of computer science that focuses on creating intelligent agents, to assess the situation and take actions to advance towards achieving defined goals. The goal of AI is to enable machines to simulate human intelligence.

• Machine Learning (ML): A subfield of Artificial Intelligence that focuses on developing algorithms that allow computers to “learn” from data without being explicitly programmed. Instead of following rigid rules, ML models learn patterns and make predictions or decisions based on the data they are trained on. AI can be either supervised, unsupervised, and reinforcement learning.

• Deep Learning (DL): A subfield of Machine Learning that uses artificial neural networks with multiple layers. Deep learning excels at learning complex patterns from large datasets, used in areas like image recognition, speech recognition, and natural language processing.

Similar and Related Terms:

• Big Data: Refers to extremely large datasets that may be analyzed computationally to reveal subtle patterns, trends, and associations, especially relating to human behavior and interactions. These datasets often exceed the size and speed of memory.

• Business Intelligence (BI): A set of strategies and technologies used for the data analysis of business information to provide actionable insights that help organizations make better business decisions. Reports and dashboards used to focused on descriptive analytics (what happened) rather than predictive or prescriptive analytics.

• Natural Language Processing (NLP): A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications like spam detection, sentiment analysis, machine translation, and chatbots.

• Computer Vision: A subfield of AI that enables computers to “see” and interpret visual information such as images and videos. Applications include facial recognition, object detection, autonomous vehicles, and medical image analysis.

• Predictive Analytics: A type of data analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Its goal is to predict what will happen.

• Prescriptive Analytics: The next step beyond predictive analytics, focusing on determining the best course of action for a given situation. It not only predicts what will happen but also suggests why it will happen and recommends actions to take to achieve a desired outcome or mitigate a risk.

• Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving rewards or penalties. The goal is to maximize the cumulative reward over time. It’s often used in robotics, game playing (like AlphaGo), and autonomous systems.

• Data Engineering: The aspect of data science that focuses on the practical applications of data collection, storage, processing, and transformation. Data engineers build and maintain data pipelines, databases, and data warehouses to allow convenient access and efficient use.

• Ethics in AI/Data Science: Moral implications, societal impact, responsible development and deployment of AI and data-driven systems in attempt to address concerns like bias, fairness, privacy, transparency, and accountability.

Differences

More differences

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R Notebook

Finding Rational Estimates of Pi

**by Robert Batzinger, Emeritus Instructor,

\[\begin{equation}N = \left\lfloor D \times \pi \right\rceil\rlap{\qquad\qquad (1)}\end{equation}\] Rational estimate of Pi is calculated as

\[\begin{equation}\pi_{est} = \frac{N}{D}\rlap{\qquad\qquad (2)}\end{equation}\]

\[\begin{equation}\chi ^ 2 = \frac{(\pi_{est}-\pi)^2}{\pi}\rlap{\qquad\qquad (3)}\end{equation}\] \[\begin{equation}\epsilon = \left|\pi_{est} - \pi\right|\rlap{\qquad\qquad (4)}\end{equation}\]