Standards in this Framework
| Standard | Description |
|---|---|
| 1.1.1 | Research career paths in AI (e.g., data scientist, machine learning engineer, and AI ethicist). |
| 1.1.2 | Communicate effectively in team-based, AI-related projects. |
| 1.2.1 | Identify key trends in AI and AI-related professional certifications (e.g., AWS AI/ML, Google TensorFlow Developer). |
| 1.2.2 | Discuss the importance of ethical responsibilities in AI professions. |
| 2.1.1 | Define artificial intelligence and its subfields (e.g., machine learning, natural language processing, computer vision, and robotics). |
| 2.1.2 | Differentiate between AI, machine learning, and deep learning. |
| 2.1.3 | Describe real-world applications of AI in various industries (e.g., healthcare, finance, education, and entertainment). |
| 2.1.4 | Discuss symbolic AI and its relevance to early AI research. |
| 2.1.5 | Compare rule-based systems to learning-based systems. |
| 2.2.1 | Summarize milestones in the history of AI development. |
| 2.2.2 | Discuss the societal impact of AI technologies, including potential benefits and risks. |
| 2.2.3 | Discuss the concept of explainable AI (XAI) and its importance. |
| 3.1.1 | Describe core AI concepts such as decision-making, uninformed and informed search algorithms, and planning. |
| 3.1.2 | Demonstrate the use of heuristic algorithms (e.g., A*, greedy algorithms) for search problems. |
| 3.1.3 | Implement basic search algorithms (e.g., breadth-first search, depth-first search). |
| 3.1.4 | Investigate optimization techniques (e.g., gradient descent). |
| 3.2.1 | Describe supervised, unsupervised, and reinforcement learning. |
| 3.2.2 | Implement a basic machine learning model (e.g., linear regression, decision tree) using a programming library. |
| 3.2.3 | Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score. |
| 3.2.4 | Develop algorithms to preprocess data (e.g., splitting into training and test datasets). |
| 3.2.5 | Implement clustering algorithms (e.g., K-Means, DBSCAN) using a programming library. |
| 3.2.6 | Apply regularization techniques (e.g., L1, L2) to address and reduce overfitting in machine learning models. |
| 4.1.1 | Collect and clean data from multiple sources (e.g., CSV, APIs, and web scraping). |
| 4.1.2 | Perform feature engineering (e.g., scaling, encoding, and handling missing values). |
| 4.1.3 | Investigate data augmentation techniques for AI models (e.g., image transformation, text tokenization). |
| 4.1.4 | Create visualizations to understand data distribution and relationships (e.g., histograms, scatter plots). |
| 4.2.1 | Describe the importance of data quality and quantity in AI applications. |
| 4.2.2 | Describe the potential for bias in training data and its impact on AI systems. |
| 4.2.3 | Investigate strategies for addressing data imbalance (e.g., oversampling, undersampling). |
| 5.1.1 | Define computer vision and its applications (e.g., image recognition, object detection, image segmentation). |
| 5.1.2 | Examine the steps involved in computer vision tasks (e.g., image acquisition, preprocessing, feature extraction, classification). |
| 5.1.3 | Analyze different image data types and formats commonly used in computer vision applications. |
| 5.2.1 | Apply basic image processing techniques (e.g., filtering, edge detection, and color manipulation) using libraries like OpenCV. |
| 5.2.2 | Extract relevant features from images (e.g., edges, corners, and textures). |
| 5.2.3 | Implement object detection algorithms (e.g., using Haar cascades or pre-trained models) to identify objects in images. |
| 6.1.1 | Define natural language processing and its applications (e.g., text summarization, machine translation, sentiment analysis, and chatbots). |
| 6.1.2 | Describe the challenges of natural language processing (e.g., ambiguity, context, variability). |
| 6.1.3 | Explain fundamental concepts (e.g., tokenization, stemming, and lemmatization) of natural language processing. |
| 6.2.1 | Implement text preprocessing techniques (e.g., removing stop words, handling punctuation). |
| 6.2.2 | Create numerical representations of text data (e.g., bag-of-words, TF-IDF). |
| 6.3.1 | Perform sentiment analysis on text data using pre-trained models or simple techniques. |
| 6.3.2 | Investigate text generation using recurrent neural network (RNN) or transformers. |
| 7.1.1 | Describe the structure of a neural network, including layers, neurons, and activation functions. |
| 7.1.2 | Implement a simple neural network using a programming framework (e.g., TensorFlow, PyTorch). |
| 7.1.3 | Visualize the training process using tools (e.g., TensorBoard or matplotlib). |
| 7.2.1 | Investigate the concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). |
| 7.2.2 | Train a CNN for image classification tasks. |
| 7.2.3 | Analyze natural language processing tasks using RNNs or transformers (e.g., sentiment analysis, text generation). |
| 7.2.4 | Investigate transfer learning and fine-tune pre-trained models for custom tasks. |
| 8.1.1 | Investigate the role of knowledge representation in AI. |
| 8.1.2 | Analyze representation methods (e.g., semantic networks, frames, and ontologies). |
| 8.1.3 | Discuss the importance of reasoning and inference in AI systems. |
| 8.2.1 | Investigate propositional logic and first-order logic. |
| 8.2.2 | Implement logical reasoning techniques (e.g., forward chaining and backward chaining) in simple scenarios. |
| 8.3.1 | Investigate reasoning under uncertainty (e.g., Bayesian networks, probabilistic reasoning). |
| 8.3.2 | Implement algorithms for reasoning with uncertainty (e.g., calculating probabilities in a Bayesian network). |
| 8.3.3 | Apply decision-making techniques in simple scenarios (e.g., using decision trees or expected value calculations). |
| 8.4.1 | Investigate how KRR is used in expert systems, recommendation engines, and natural language understanding. |
| 8.4.2 | Investigate real-world applications, such as knowledge graphs and automated reasoning systems (e.g., IBM Watson). |
| 8.4.3 | Use tools and libraries for KRR (e.g., Resource Description Framework and Web Ontology Language). |
| 9.1.1 | Analyze case studies of AI misuse (e.g., facial recognition, social media algorithms). |
| 9.1.2 | Propose strategies for ensuring fairness and reducing bias in AI systems. |
| 9.1.3 | Investigate legal and regulatory frameworks around AI in different industries. |
| 9.2.1 | Describe the importance of transparency and interpretability in AI. |
| 9.2.2 | Identify methods to protect user privacy in AI applications. |
| 9.2.3 | Investigate tools for auditing and mitigating bias in AI models (e.g., Fairlearn, SHAP). |
