classMateAI/test.json at master · Alex2099v1/classMateAI · GitHub

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{
    "content": [
      {
        "subject_name": "Machine Learning",
        "subject_description": "Algorithms that allow systems to learn from data.",
        "topics": [
          {
            "topic_name": "Gradient Descent",
            "topic_description": "Optimization algorithm for finding minima of functions.",
            "source_locations": [
              {
                "filename": "ml_lecture_notes.pdf",
                "filepath": "/university/cs501/notes/",
                "page": 15,
                "location_description": "Section 3: Optimization"
              },
              {
                "filename": "deep_learning_book.pdf",
                "filepath": "/books/ai/",
                "page": 85,
                "location_description": "Chapter 4.3"
              }
            ],
            "subtopics": [
              {
                "subtopic_name": "Stochastic Gradient Descent",
                "subtopic_description": "Updates parameters using one sample at a time.",
                "source_locations": [
                  {
                    "filename": "ml_lecture_notes.pdf",
                    "filepath": "/university/cs501/notes/",
                    "page": 16,
                    "location_detail": "Paragraph 2",
                    "keywords": "SGD, online learning, variance"
                  },
                  {
                    "filename": "sgd_paper.pdf",
                    "filepath": "/papers/ml/",
                    "page": 3,
                    "location_detail": "Algorithm 1",
                    "keywords": "update rule, convergence"
                  }
                ]
              },
              {
                "subtopic_name": "Batch Gradient Descent",
                "subtopic_description": "Updates parameters using the entire dataset.",
                "source_locations": [
                  {
                    "filename": "ml_lecture_notes.pdf",
                    "filepath": "/university/cs501/notes/",
                    "page": 15,
                    "location_detail": "Comparison Table"
                  }
                ]
              }
            ]
          },
          {
            "topic_name": "Linear Regression",
            "topic_description": "Predicting a continuous value based on linear relationships.",
            "source_locations": [
              {
                "filename": "stats_intro.pdf",
                "filepath": "/university/stats101/",
                "page": 112
              }
            ],
            "subtopics": [
              {
                "subtopic_name": "Ordinary Least Squares",
                "subtopic_description": "Method for estimating parameters by minimizing the sum of squared residuals.",
                "source_locations": [
                  {
                    "filename": "stats_intro.pdf",
                    "filepath": "/university/stats101/",
                    "page": 115,
                    "location_detail": "Equation 5.3",
                    "keywords": "OLS, residuals, cost function"
                  }
                ]
              }
            ]
          }
        ]
      },
      {
        "subject_name": "Calculus",
        "subject_description": "The mathematical study of continuous change.",
        "topics": [
          {
            "topic_name": "Differentiation",
            "topic_description": "Finding the rate at which a quantity changes.",
            "source_locations": [
              {
                "filename": "calc_textbook.pdf",
                "filepath": "/books/math/",
                "page": 75,
                "location_description": "Chapter 3 Introduction"
              }
            ],
            "subtopics": [
              {
                "subtopic_name": "Chain Rule",
                "source_locations": [
                  {
                    "filename": "calc_textbook.pdf",
                    "filepath": "/books/math/",
                    "page": 92,
                    "location_detail": "Theorem 3.5",
                    "keywords": "composition, derivative, inner function, outer function"
                  }
                ]
              }
            ]
          }
        ]
      }
    ],
    "mistakes": [
      {
        "source_filename": "ml_homework_2.pdf",
        "source_filepath": "/university/cs501/homework/",
        "page": 2,
        "location_detail": "Problem 1, Part (b), Line 5",
        "description": "Learning rate set too high, caused divergence.",
        "type": "Hyperparameter Tuning",
        "details": "The cost function increased instead of decreasing. Needed to reduce alpha from 0.1 to 0.01.",
        "relevant_topic": "Gradient Descent",
        "relevant_subtopic": "Stochastic Gradient Descent"
      },
      {
        "source_filename": "calc_exam_1.pdf",
        "source_filepath": "/exams/math/",
        "page": 1,
        "location_detail": "Question 3",
        "description": "Forgot to apply the chain rule to the inner function.",
        "type": "Procedural Error",
        "details": "Differentiated outer function correctly, but missed multiplying by derivative of e^(2x).",
        "relevant_topic": "Differentiation",
        "relevant_subtopic": "Chain Rule"
      },
      {
        "source_filename": "ml_homework_1.pdf",
        "source_filepath": "/university/cs501/homework/",
        "page": 3,
        "location_detail": "Problem 4",
        "description": "Used incorrect formula for OLS.",
        "type": "Conceptual Error",
        "details": "Mixed up the terms in the normal equation.",
        "relevant_topic": "Linear Regression",
        "relevant_subtopic": "Ordinary Least Squares"
      },
      {
        "source_filename": "calc_exam_1.pdf",
        "source_filepath": "/exams/math/",
        "page": 4,
        "location_detail": "Question 7",
        "description": "Arithmetic error during simplification.",
        "type": "Calculation",
        "details": "Made a mistake adding fractions at the end.",
        "relevant_topic": "Differentiation"
      }
    ]
  }