Session 2 Machine Learning Fundamental PDF

MACHINE LEARNING FUNDAMENTAL SESSION 02 SUBJECT MATTER EXPERT Henry Lucky, S.Kom., M.Kom. LEARNING OUTCOMES SESSION 2 At the end of this session, students will be able to: LO 1: Describe what is AI, its applications, use cases, and how it is transforming organizations LO...

MACHINE LEARNING FUNDAMENTAL SESSION 02 SUBJECT MATTER EXPERT Henry Lucky, S.Kom., M.Kom. LEARNING OUTCOMES SESSION 2 At the end of this session, students will be able to: LO 1: Describe what is AI, its applications, use cases, and how it is transforming organizations LO 2: Explain terms like machine learning, deep learning, neural networks, natural language processing, computer vision, speech recognition, etc. OUTLINE SESSION 2 Introduction to Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning INTRODUCTION TO MACHINE LEARNING WHAT IS MACHINE LEARNING? Machine learning is the cornerstone of AI, considered fundamental by many experts. AI applications are expected to possess machine learning capabilities to be deemed truly intelligent. Simply being able to perform tasks like chess-playing doesn't signify intelligence; machine learning is essential for advanced abilities. In essence, a chess-playing system lacking machine learning is akin to a basic computer program, lacking true intelligence. So, what is machine learning? INTRODUCTION TO MACHINE LEARNING WHAT IS MACHINE LEARNING? What is Machine Learning? Source: https://youtu.be/ukzFI9rgwfU INTRODUCTION TO MACHINE LEARNING THE VON NEUMANN MACHINE A computer architecture proposed by polymath in 1945 based on Sir Alan Turing’s work on Turing Machine. The basic components of vNM which consists of:  Central processing unit (CPU) – system brain consists of system control unit, basic arithmetic, and logical unit  Memory unit - data and information storage  Input and output units – data input (e.g. keyboard) and output (e.g. screen) INTRODUCTION TO MACHINE LEARNING THE VON NEUMANN MACHINE INTRODUCTION TO MACHINE LEARNING THE VON NEUMANN MACHINE All computer systems that we use today; their basic architectures are all inherited from the blueprint of von Neumann Machine. Are they intelligent machines? Why or why not? Let’s look at two cases of computer systems in our daily activities. INTRODUCTION TO MACHINE LEARNING CASE 1: NUMERICAL WEATHER PREDICTION Weather forecasting relies on Numerical Weather Prediction (NWP) systems, which utilize complex mathematical models and global weather observations. NWP accurately predicts weather components like air pressure, temperature, humidity, and wind speed for up to 7 days into the future. However, NWP lacks intelligence and cannot learn or improve autonomously. The system doesn’t have the ability to adapt or learn from new information. INTRODUCTION TO MACHINE LEARNING CASE 2: ALPHAGO AlphaGo, developed by DeepMind Technologies and acquired by Google in 2015, is an AI-based computer program designed to play the board game Go. AlphaGo's ability to learn and improve its gameplay distinguishes it from standard von Neumann Machines. Similar to playing against an experienced chess master, AlphaGo continuously adapts its strategies based on past experiences and opponent moves. This is what we call machine learning – intelligence. INTRODUCTION TO MACHINE LEARNING APPLICATIONS Healthcare: Diagnostics: Predictive models for disease detection (e.g., cancer, diabetes). Personalized Medicine: Treatment plans based on patient data. Medical Imaging: Automated analysis of X-rays, MRIs. Finance: Fraud Detection: Identifying fraudulent transactions. Algorithmic Trading: Automated, high-frequency trading strategies. Credit Scoring: Assessing creditworthiness using ML models. INTRODUCTION TO MACHINE LEARNING APPLICATIONS Retail: Recommendation Systems: Personalized product recommendations (e.g., Amazon, Netflix). Inventory Management: Predictive analytics for stock optimization. Customer Segmentation: Targeted marketing based on customer behaviour. Other Advanced Applications: Autonomous vehicle, Natural Language Processing, Computer Vision Smart Cities, Agriculture, Education etc. INTRODUCTION TO MACHINE LEARNING PILLARS OF MACHINE LEARNING REINFORCEMENT Source: https://towardsdatascience.com/coding-deep- learning-for-beginners-types-of-machine-learning- b9e651e1ed9d SUPERVISED LEARNING DEFINITION Supervised Learning involves learning from labeled data, where the algorithm is trained on input-output pairs. Common supervised learning tasks include classification (predicting categories) and regression (predicting continuous values). Source: https://alanjeffares.wordpress.com/2018/07/24/test/ UNSUPERVISED LEARNING DEFINITION Unlike supervised Learning, it doesn't rely on labeled data for training. Instead, it explores patterns and structures within Source: https://alanjeffares.wordpress.com/2018/07/24/test/ unlabeled data. It aim to uncover hidden patterns, groupings, or relationships in data. Common unsupervised learning tasks include clustering, dimensionality reduction, and anomaly detection. REINFORCEMENT LEARNING DEFINITION In reinforcement learning, an agent learns to make decisions by interacting with an environment. Unlike other types of learning, reinforcement learning doesn't need labeled or unlabeled data - it learns from trial and error. The goal for the agent is to learn the best actions to take in different situations to maximize rewards. It's like teaching a pet tricks: they learn what actions lead to treats and repeat those actions. Even ChatGPT is built using some kind of reinforcement learning! REINFORCEMENT LEARNING EXAMPLES: A.I. LEARNS TO PLAY FLAPPY BIRD REINFORCEMENT LEARNING EXAMPLES: MULTI-AGENT HIDE AND SEEK DEEP LEARNING AI VS ML VS DL DEEP LEARNING NEURON A human brain is a complex organization of a cell called as neuron. A neuron can process information by running this process: Send new Receive signals Tweak the signals to other from other signals in its neurons via neurons via cell body, synaptic dendrites, terminals DEEP LEARNING ARTIFICIAL NEURAL NETWORK ANNs are a computational model inspired by the structure and function of biological neural networks in the human brain. Here is the comparation between biological neuron and artificial neuron: DEEP LEARNING ARTIFICIAL NEURAL NETWORK In ANN, the neurons are interconnected and organized in layers. Information flows through the network from input layers to output layers. Typical ANN has 3 layers: Input, Hidden and Output layers. DEEP LEARNING DEFINITION Deep learning revolves around ANN with multiple hidden layers (hence "deep"), It can learn complex patterns in data. It can automatically learn features from raw data, eliminating the need for manual feature extraction. CASE STUDY 3 MACHINE LEARNING METHODS The 3 learning methods in machine learning are always integrated and mixed with our daily learning activities, including: o Attending lectures; o Reading books and papers; o Completing assignments and exercises; o Participating in group discussions; o Completing term tests and quizzes; o Study and revision; Identify the learning methods involved in each activity above and give the reasoning! REFERENCES SESSION 2 Raymond S. T. Lee. 2020. Artificial Intelligence in Daily Life. Springer. China. ISBN 9789811576959 A visual and interactive guide to the basics of neural network http://jalammar.github.io/visual-interactive-guide-basics-neural-n etworks/ Supervised-Unsupervised-Reinforcement learning concept https://youtu.be/mMc_PlemSnU