diff --git a/doc/pub/week48/html/week48-bs.html b/doc/pub/week48/html/week48-bs.html index ce7e9f3ae..1c2786548 100644 --- a/doc/pub/week48/html/week48-bs.html +++ b/doc/pub/week48/html/week48-bs.html @@ -8,8 +8,8 @@ doconce format html week48.do.txt --html_style=bootstrap --pygments_html_style=d - -Week 48: Support Vector Machines and Summary of course + +Week 48: Gradient boosting and summary of course @@ -143,7 +143,7 @@ doconce format html week48.do.txt --html_style=bootstrap --pygments_html_style=d None, 'choose-a-model-and-algorithm'), ('Preparing Your Data', 2, None, 'preparing-your-data'), - ('Which Activation and Weights to Choose in Neural Networks', + ('Which activation and weights to choose in neural networks', 2, None, 'which-activation-and-weights-to-choose-in-neural-networks'), @@ -258,7 +258,7 @@ MathJax.Hub.Config({ - Week 48: Support Vector Machines and Summary of course + Week 48: Gradient boosting and summary of course
@@ -214,7 +218,7 @@ MathJax.Hub.Config({ Plans for the lecture Monday 25 November, with video suggestions etc

    -

  1. Bossting and gradient boosting and ensemble models
    2. +

  2. Boosting and gradient boosting and ensemble models

  3. Summary of course

  4. Readings and Videos:
      @@ -1249,7 +1253,7 @@ plt.show()

    1. Gradient methods for data optimization

    2. Estimation of errors using cross-validation, bootstrapping and jackknife methods;

    3. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
      4. -

    4. Principal Component Analysis to reduce the number of features.
      5. +

    5. Not discussed: Principal Component Analysis to reduce the number of features.
    @@ -1281,7 +1285,7 @@ plt.show()

  5. Boosting and gradient boosting

-

  • Support vector machines +

  • Not discussed this year: Support vector machines

    1. Binary classification and multiclass classification

    2. Kernel methods
      3. @@ -1308,12 +1312,12 @@ ethical conduct is emphasized throughout the course. @@ -1322,7 +1326,7 @@ ethical conduct is emphasized throughout the course.

      1. Rapidly emerging application area
        2. -

      2. Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
        3. +

      3. Experiment AND theory are evolving in many many fields.

      4. Requires education/retraining for more widespread adoption

      5. A lot of “word-of-mouth” development methods
      @@ -1354,7 +1358,7 @@ Neural Networks, etc.

      1. Identify problem type: classification, regression

      2. Consider your data carefully
        3. -

      3. Choose a simple model that fits 1. and 2.
        4. +

      4. Choose a simple model that fits 1 and 2

      5. Consider your data carefully again! Think of data representation more carefully.

      6. Based on your results, feedback loop to earliest possible point
      @@ -1400,10 +1404,10 @@ Neural Networks, etc.
      -

      Which Activation and Weights to Choose in Neural Networks

      +

      Which activation and weights to choose in neural networks

        -

      1. RELU? ELU?
        2. +

      2. RELU? ELU? GELU? etc

      3. Sigmoid or Tanh?

      4. Set all weights to 0?
          5. @@ -1461,17 +1465,17 @@ set of hyperparameters and regularization methods.

          Other courses on Data science and Machine Learning at UiO

          -

          The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.

          -
            -

          1. STK2100 Machine learning and statistical methods for prediction and classification.
            2. -

          2. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
            3. +

          3. FYS5429 – Advanced machine learning and data analysis for the physical sciences
            4. +

          4. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI

          5. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.

          6. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.

          7. STK-IN4300 – Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
            8. -

          8. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
            9. -

          9. IN5400/INF5860 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
            10. -

          10. TEK5040 – Dyp læring for autonome systemer. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
            11. +

          11. IN-STK5000 Responsible Data Science. Methods for adaptive collection and processing of data based on machine learning techniques.
            12. +

          12. IN4310 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
            13. +

          13. IN5310 – Advanced Deep Learning for Image Analysis
            14. +

          14. IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
            15. +

          15. TEK5040 – Deep learning for autonomous systems. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
          diff --git a/doc/pub/week48/html/week48-solarized.html b/doc/pub/week48/html/week48-solarized.html index 2917f8527..98b14445d 100644 --- a/doc/pub/week48/html/week48-solarized.html +++ b/doc/pub/week48/html/week48-solarized.html @@ -8,8 +8,8 @@ doconce format html week48.do.txt --pygments_html_style=perldoc --html_style=sol - -Week 48: Support Vector Machines and Summary of course + +Week 48: Gradient boosting and summary of course @@ -170,7 +170,7 @@ div.toc p,a { None, 'choose-a-model-and-algorithm'), ('Preparing Your Data', 2, None, 'preparing-your-data'), - ('Which Activation and Weights to Choose in Neural Networks', + ('Which activation and weights to choose in neural networks', 2, None, 'which-activation-and-weights-to-choose-in-neural-networks'), @@ -279,7 +279,7 @@ MathJax.Hub.Config({
          -

          Week 48: Support Vector Machines and Summary of course

          +

          Week 48: Gradient boosting and summary of course

          @@ -288,11 +288,11 @@ MathJax.Hub.Config({
          -Department of Physics, University of Oslo, Norway +Department of Physics and Center for Computing in Science Education, University of Oslo, Norway

          -

          Nov 21, 2024

          +

          Nov 23, 2024


          @@ -307,7 +307,9 @@ MathJax.Hub.Config({

          • Work and Discussion of project 3
            • -
          • Last weekly exercise,
            • +
          • Last weekly exercise
            • +
          • Lab sessions at usual times.
            • +
          • For the week of December 2-6, lab sessions atart at 10am and end 4pm, room FØ434, Tuesday and Wednesday
          @@ -316,7 +318,7 @@ MathJax.Hub.Config({ Plans for the lecture Monday 25 November, with video suggestions etc

            -
          1. Bossting and gradient boosting and ensemble models
            2. +
          2. Boosting and gradient boosting and ensemble models
          3. Summary of course
          4. Readings and Videos:
              @@ -1266,7 +1268,7 @@ plt.show()
            1. Gradient methods for data optimization
            2. Estimation of errors using cross-validation, bootstrapping and jackknife methods;
            3. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
              4. -
            4. Principal Component Analysis to reduce the number of features.
              5. +
            5. Not discussed: Principal Component Analysis to reduce the number of features.










            Machine learning

            @@ -1293,7 +1295,7 @@ plt.show()
          5. Random forests
          6. Boosting and gradient boosting
          -
        • Support vector machines +
        • Not discussed this year: Support vector machines
          1. Binary classification and multiclass classification
          2. Kernel methods
            3. @@ -1317,19 +1319,19 @@ ethical conduct is emphasized throughout the course.
            • Understand linear methods for regression and classification;
            • Learn about neural network;
              • -
            • Learn about bagging, boosting and trees
              • -
            • Support vector machines
              • +
            • Learn about bagging, boosting and trees +
            • Learn about basic data analysis;
            • Be capable of extending the acquired knowledge to other systems and cases;
            • Have an understanding of central algorithms used in data analysis and machine learning;
              • -
            • Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.
              • +
            • Work on numerical projects to illustrate the theory. The projects play a central role.










            Perspective on Machine Learning

            1. Rapidly emerging application area
              2. -
            2. Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
              3. +
            3. Experiment AND theory are evolving in many many fields.
            4. Requires education/retraining for more widespread adoption
            5. A lot of “word-of-mouth” development methods
            @@ -1357,7 +1359,7 @@ Neural Networks, etc.
            1. Identify problem type: classification, regression
            2. Consider your data carefully
              3. -
            3. Choose a simple model that fits 1. and 2.
              4. +
            4. Choose a simple model that fits 1 and 2
            5. Consider your data carefully again! Think of data representation more carefully.
            6. Based on your results, feedback loop to earliest possible point
            @@ -1390,10 +1392,10 @@ Neural Networks, etc.
          3. When to do train/test split?










          -

          Which Activation and Weights to Choose in Neural Networks

          +

          Which activation and weights to choose in neural networks

            -
          1. RELU? ELU?
            2. +
          2. RELU? ELU? GELU? etc
          3. Sigmoid or Tanh?
          4. Set all weights to 0?
              5. @@ -1436,17 +1438,17 @@ set of hyperparameters and regularization methods.









              Other courses on Data science and Machine Learning at UiO

              -

              The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.

              -
                -
              1. STK2100 Machine learning and statistical methods for prediction and classification.
                2. -
              2. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
                3. +
              3. FYS5429 – Advanced machine learning and data analysis for the physical sciences
                4. +
              4. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI
              5. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
              6. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
              7. STK-IN4300 – Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
                8. -
              8. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
                9. -
              9. IN5400/INF5860 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
                10. -
              10. TEK5040 – Dyp læring for autonome systemer. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
                11. +
              11. IN-STK5000 Responsible Data Science. Methods for adaptive collection and processing of data based on machine learning techniques.
                12. +
              12. IN4310 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
                13. +
              13. IN5310 – Advanced Deep Learning for Image Analysis
                14. +
              14. IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems
                15. +
              15. TEK5040 – Deep learning for autonomous systems. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.










              Additional courses of interest

              diff --git a/doc/pub/week48/html/week48.html b/doc/pub/week48/html/week48.html index 6004b819b..93cdc9f13 100644 --- a/doc/pub/week48/html/week48.html +++ b/doc/pub/week48/html/week48.html @@ -8,8 +8,8 @@ doconce format html week48.do.txt --pygments_html_style=default --html_style=blo - -Week 48: Support Vector Machines and Summary of course + +Week 48: Gradient boosting and summary of course