AI/Big Data

𝗔𝗜 𝗘𝘁𝗵𝗶𝗰𝘀: Emphasises the importance of ethical considerations in AI development, including bias prevention and accountability.

- 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Explains the algorithms that enable machines to learn from data, highlighting examples such as fraud detection and image recognition.

- 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮: Addresses the vast volumes of data processed and the importance of data management and analysis for valuable insights.

- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: Covers the entire spectrum of extracting knowledge from data, including predictive modelling and data mining.

- 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀: Discusses the architecture inspired by the human brain that's at the core of many AI functions, especially in pattern recognition.

- 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 & 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Distinguishes between learning with labelled data and discovering hidden patterns without labelled data.

- 𝗧𝗲𝘅𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 & 𝗡𝗟𝗣: Delves into the processing of human language, sentiment analysis, and translation, showcasing how AI can understand and generate human language.

- 𝗚𝗔𝗡 (𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸): Introduces the concept of competing neural networks, which can generate new data that mimics real-world distributions.

- 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Describes how machines interpret visual information from the world, applied in areas like facial recognition and autonomous driving.

- 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Emphasises the importance of interpreting data to make informed decisions, and the visualisation of complex data to make it understandable at a glance.

- 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Focuses on using historical data to predict future outcomes, essential for risk assessment and strategic planning.