AI Foundations for Decision Makers: From Zero to LLMs

Define AI through four quadrants: acting humanly, thinking humanly, thinking rationally, and acting rationally, explore the Turing test, and the concept of intelligent agents and systems.

Differentiate narrow AI from broad AI by explaining domain-specific pattern recognition and automation versus AGI that generalizes across tasks, and note current limits and economic impacts.

Compare rule-based traditional software with data-driven machine learning, noting how machine learning learns from data to improve, scale, and adapt, as shown in tasks like language translation.

Explore the major categories of machine learning: supervised, unsupervised, and reinforcement, covering classification, regression, clustering, dimensionality reduction, and learning from data streams to maximize rewards.

Explore how data underpins machine learning by leveraging features, labels, and datasets, with careful feature selection, formatting, and the role of feature engineering in transforming data for predictions.

Explore how labels define outcomes in supervised learning, map features to the target via a learned function, and highlight data labeling with examples like image classification and sentiment analysis.

Explore how datasets form the foundation of model training by organizing features and labels, creating training, validation, and testing sets, and applying data augmentation to improve generalization.

Identify common machine learning challenges, focusing on overfitting, bias, and generalization. Learn how model complexity, data size, and noise impact performance on unseen data, and apply regularization and data improvements.

Explore bias and underfitting as high bias models oversimplify problems, affecting generalization in machine learning, with a house-price linear regression example. Mitigate bias by improving data diversity and labeling.

Develop a clear understanding of generalization, its relation to overfitting, and how diverse data, data quality, simpler models, and regularization improve performance on unseen inputs, illustrated by Zillow's Zestimate case.

Master feature engineering for artificial intelligence models by selecting relevant features, creating and extracting new features from data, and preprocessing with scaling, encoding, and time series and image data techniques.

Identify primary and secondary data sources—user input, logs, enterprise data, sensor data, web content, and open data repositories—and learn to assess quality, limitations, and biases.

Explore model selection for diverse machine learning tasks by comparing rule-based systems, trees, ensembles, linear and logistic regression, SVMs, neural networks, CNNs, RNNs, and transformers.

Identify the problem type (supervised or unsupervised) and data characteristics to select suitable models, from decision trees to neural networks, probabilistic models, and ensembles, for images, text, and sequences.

Tune hyperparameters with a validation set to improve generalization and detect overfitting. Use cross-validation, including k-fold and holdout, for robust performance estimates and final evaluation on unseen data.

Evaluate machine learning performance across problem types using classification metrics—accuracy, precision, recall, and F1. Apply regression, ranking, and clustering metrics to capture broader evaluation perspectives.

Explore how hyperparameters govern training dynamics, model capacity, and regularization, including learning rate, batch size, dropout, and weight decay, and how tuning shapes accuracy and generalization.

Regularization prevents overfitting and improves generalization by balancing model complexity and data fit. Techniques include L1 and L2 penalties, dropout, sparsity regularization, early stopping, and data augmentation.

Explore how neural networks learn by forward propagation, backpropagation, and gradient descent, minimizing loss via the cost function while using activation functions, with regularization to boost generalization and curb overfitting.

Explore how artificial neural networks learn from data using neurons, layers, activation functions, and weights and biases, with backpropagation guiding optimization to make predictions.

Explore activation functions that inject non-linearity into neural networks, enabling learning of complex patterns beyond linear regression, including ReLU, sigmoid, tanh, softmax, and issues like vanishing gradients.

Explore optimization algorithms that train machine learning models by minimizing the cost function, comparing batch, stochastic, and mini-batch gradient descent, and integrating momentum, Nesterov accelerated gradient, RMSprop, and Adam.

Explore natural language processing basics, from language models to text processing techniques like tokenization, feature extraction, and tf-idf, including stemming and lemmatization.

Discover natural language processing applications such as text classification, information extraction, text generation, language modeling, speech recognition, question answering, chatbots, and text summarization, driven by large language models via prompting.

Explore core NLP tasks, named entity recognition, sentiment analysis, POS tagging, and syntactic and semantic parsing, and see how NER detects entities and parsing reveals meaning.

Explore traditional language models, including n-gram models and hidden Markov models, detailing how they predict word sequences from preceding tokens, with training by counting and data sparsity.

Explore the challenges in natural language processing, including polysemy, ambiguity, low-resource languages, and bias in language models, and learn evaluation methods for long-context LMS.

Explore perplexity as a fundamental metric that measures how well a language model predicts the next token, guiding improvements in model size, data, and context length, while noting its limitations.

Explore encoder-decoder architecture for sequence-to-sequence tasks, where the encoder converts input into a context vector and the decoder outputs the sequence, with attention and training techniques like teacher forcing.

Review the fundamentals of machine learning and AI and introduce large language models, including attention transformers, pre-training, fine-tuning, prompting, Rag, and deployment.

Explore how the attention mechanism lets models focus on important words and filter noise. See how self-attention and encoder-decoder ideas underpin transformer models for language, image tasks, and medical AI.

Explore how the transformer uses self-attention to weigh all words in a sequence, enabling context-rich representations, faster training, and long-range dependencies without recurrence.

Explore how self-attention and cross-attention power transformers, using query, key, and value vectors to capture intra-sequence relationships and cross-sequence relevance.

Explore scaling laws for large language models, where test loss follows a power law with compute, data, and model size, guiding optimal resource use.

Examine how llms learn from pre-training, fine tuning, and post training, building general language knowledge, task adaptation, and human-aligned outputs via instruction tuning and feedback.

Self-supervised learning pre-trains large language models from unlabeled data; encoder-only, decoder-only, and encoder-decoder architectures use CLM, MLM, and denoising or span corruption objectives.

Explore encoder-only, decoder-only, and encoder-decoder architectures that shape language understanding and generation, with bidirectional versus unidirectional attention and objectives like MLM and causal language modeling.

Balance model size with task complexity and deployment needs; smaller models offer interpretability, while larger models enable capabilities. Use quantization, pruning, distillation, and parameter efficient fine tuning to optimize performance.

Explore fine tuning fundamentals, including full tuning, pbft, and prompt-related methods, to update model parameters with task-specific data. See how instruction tuning enables domain adaptation and improves generalization.

Parameter-efficient fine-tuning enables adapting large language models by training only a small subset of parameters and using adapters, prompt tuning, and quantization.

Align post training with human values to ensure safe, coherent, and helpful language model responses through reinforcement learning from human feedback and AI feedback, knowledge distillation, and fine tuning.

Explore ways to interact with large language models through prompting, question answering, and agents, and apply prompt engineering with task specification, generation conditions, and in-context demonstrations.

Explore zero-shot prompting and chain-of-thought techniques, then extend to few-shot prompting and retrieval strategies, including self-consistency and tree-of-thought prompting, to boost LLM reasoning.

Explore chain-of-thought reasoning to break problems into sequential steps, improving interpretability and accuracy in llms, with zero-shot prompting guiding multi-step math, logic, and decision making.

Demonstrates zero-shot chain-of-thought prompting with think step by step, using five apples at $1.2 each from a $10 bill to yield $4 change.

Discover how few-shot prompting uses a few input-output examples to guide responses, improve accuracy on pattern recognition, and support contextual learning, with results influenced by example quality and order.

Apply chain-of-thought with few-shot prompts to enable step-by-step reasoning demonstrations, boosting llms to tackle complex logical tasks, as shown by a simple 100 gadgets per hour example.

Demonstrate-search-predict decomposes complex questions into sub questions, retrieves relevant information, and synthesizes responses with few-shot prompting to guide final answers and improve accuracy.

Explore interleaved retrieval guided by chain-of-thought to perform multi-hop question answering by interleaving retrieval and reasoning steps. See how iterative retrieval refines facts and improves accuracy for complex queries.

Master self-consistency and tree of thought as advanced prompting methods that boost reasoning accuracy through multi-path evaluation, majority voting, and adaptive prompts for multi-step problem solving.

Use retrieval augmented generation to integrate external knowledge and reduce hallucinations, improving factual accuracy. Rag combines retrieval, augmentation, and generation with knowledge graphs and retrieval techniques for verifiable, up-to-date responses.

Explore the GPT series by OpenAI, tracing the generative pre-trained transformer models from GPT-2 to GPT-4, and examine zero-shot and few-shot learning, multimodal capabilities, and efficiency improvements.

Explore Google's T5, a text-to-text transfer transformer with an encoder-decoder design and span corruption pre-training, and compare UL2’s mixture of denoisers for multi-task NLP.

Explore Claude by Anthropic, a safety-focused AI guided by constitutional AI and reinforcement learning from human feedback to align with human values and intentions, emphasizing safety, interpretability, bias mitigation, guardrails.

Discover Meta's Llama family of open source large language models, emphasizing efficiency, fine tunability, context and window size, with variants like Llama 2 and 3, Alpaca, Vicuna, and Code Llama.

Explore Mistral models, highlighting efficiency through mixture of experts, precision optimization, grouped query attention, and parallelism to reduce costs and boost long-context performance.

Gemini, Google DeepMind's multimodal AI, processes text, images, audio, and video to enable integrated reasoning and context-rich insights, powered by transformer architecture and a mixture of experts.

Explore DeepSeek r1, a transformer-based large language model by Deep Sky, trained with supervised learning and reinforcement learning from human feedback for text, code, translation, and question answering.

Grok xAI integrates with the X platform to deliver real-time data and up-to-the-minute insights, while grok three enhances reasoning, context management, and multimodal capabilities for structured decision making.

Explore enterprise LLMs like Command Plus and other models that use retrieval augmented generation to ground outputs in external knowledge for accuracy and domain-specific responses.

Evaluate latency versus accuracy, cost, safety, bias, and compliance when selecting lm models for real world apps, balancing inference time and throughput. Apply quantization and other optimizations to improve efficiency.

Assess LM performance for decision makers using benchmarks like MLU, Big Bench, GLUE, Squad, GSM8K, and code-generation evaluation to measure general knowledge, reasoning, and language understanding.

examine LM leaderboards as benchmarking platforms comparing AI models on accuracy, efficiency, robustness, and ethics. they feature open, multi-turn, and code benchmarks, and discuss challenges like overfitting and bias.

Explore core technical challenges in LLMs, including hallucinations, memorization, exposure bias, and long-context limits, and learn mitigation strategies like alignment, retrieval, and grounded generation.

Review the journey from AI and machine learning basics to large language models, prompting, and agent economy, highlighting attention, fine tuning, evaluation, deployment, and ethical considerations.

Explore AI agents that act as autonomous assistants, using large language models to understand text, images, and sounds, connect to systems to perform actions and automate tasks.

Explain why ai agents matter in the ai first future, enabling scalable operations, automating cognitive tasks, and augmenting human decision making while enabling new business models.

Explore major examples of AI agents, from general autonomous agents and Lang Chain to Auto GPT, tool-use and code-generation agents, across software engineering, science, and UI tasks.

Classify ai agents by task, autonomy, and embodiment, from task-specific and multi-agent systems to autonomous, semi-autonomous, and embodied robots. From customer support bots to self-driving cars, these agents drive efficiency.

Explore how artificial intelligence agents autonomously perceive their environment, reason about information, and act to achieve goals through perception, reasoning, and action cycles, with memory, LLM-powered capabilities, and tools.

Explore how AI agents reshape organizations through automated workflows, multi-agent collaboration, and decentralized agent networks and DAOs, with monetization models and agent ops guiding the evolving economy.

Explore how agent ops applies devops and ml ops principles to life cycle of autonomous AI agents. Learn to improve observability, traceability, evaluation, and monitoring for reliable, efficient AI deployments.

Explore business use cases of AI agents—from sales automation and customer service to finance, healthcare, and content generation—driving productivity, workflow automation, and data-driven decision making.

Navigate the ethical and practical challenges of AI agents, including bias, reliability, hallucinations, autonomy, security, privacy, and regulatory compliance, with proactive mitigation and human-in-the-loop safeguards.

Discover how the convergence of large language models, multimodal AI, and automation enables AI agents to perceive, reason, and automate end-to-end workflows across industries.

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