update links to machine learning notes

Author: simonkirbysimonkirby

individual_modules/section_landing_pages/introduction_to_machine_learning.md

@@ -15,25 +15,25 @@ Welcome to Intro to Machine Learning, a course designed to introduce you to core
 
 * Both [*Introduction to Python*](introduction_to_python.md) and [*Python for Data Analysis*](python_for_data_analysis.md) are pre-requisites for attending this course. If you have not attended these courses, please review the full course materials in your own time.
 * As a minimum, you should be comfortable performing data analysis in `Numpy` and `Pandas`, and in creating plots with `Matplotlib`, without the need for regular guidance or support. Familiarity with Jupyter Notebooks is highly recommended.
-* In addition, we also recommend that you are comfortable using virtual environments: please see our self-study guide to do this.
+* In addition, we also recommend that you are comfortable using virtual environments: please see our [self-study guide](../../short_courses/virtual_environments.ipynb) to do this.
 
 ## Session content
 
-This course will be delivered via slides, which can be found [here](https://github.com/coding-for-reproducible-research/CfRR_Courses/tree/main/individual_modules/introduction_to_machine_learning/slides), and the tutorials via Jupyter Notebooks (these are the self-study notes).
+This course will be delivered via a mix of slides and programming tutorials. Materials can be found at the following links:
 
 ### Session 1
 
-* Slides: what is machine learning?
-* Tutorial: linear regression
-* Slides: model selection and evaluation
+* Part 1: What is machine learning? - [Click for slides](../../_static/intro_to_ml_slides/intro_to_ml_part_1.pdf)
+* Part 2: Linear regression - [Click for Jupyter Notebook](../introduction_to_machine_learning/1_linear_regression.ipynb)
+* Part 3: Model selection and evaluation - [Click for slides](../../_static/intro_to_ml_slides/intro_to_ml_part_2.pdf)
 
 ### Session 2
 
-* Tutorial: model selection and evaluation
-* Slides: the machine learning pipeline
-* Tutorial: machine learning pipeline task
+* Part 1: Model selection and evaluation - [Click for Jupyter Notebook](../introduction_to_machine_learning/2_model_selection.ipynb)
+* Part 2: The machine learning pipeline - [Click for slides](../../_static/intro_to_ml_slides/intro_to_ml_part_3.pdf)
+* Part 3: Machine learning pipeline task - [Click for Jupyter Notebook](../introduction_to_machine_learning/3_pipeline_task.ipynb)
 
 ### Session 3
 
-* Tutorial continued: machine learning pipeline task
-* Tutorial: unsupervised learning
+* Continued: Machine learning pipeline task - [Click for Jupyter Notebook](../introduction_to_machine_learning/3_pipeline_task.ipynb)
+* Part 2: Unsupervised learning - [Click for Jupyter Notebook](../introduction_to_machine_learning/4_unsupervised_learning.ipynb)

programme_information/introduction_to_machine_learning.ipynb

@@ -99,16 +99,12 @@
     "\n",
     "## Self study materials \n",
     "\n",
-    "The self-study material for this course is available [here](../individual_modules/section_landing_pages/introduction_to_machine_learning.md).\n",
+    "This course will be delivered via a mixture of slides and programming tutorials. All materials for this course can be found and downloaded from the course landing page [here](../individual_modules/section_landing_pages/introduction_to_machine_learning.md).\n",
     "\n",
     "## Developers\n",
     "\n",
     "The developer of this course is **Simon Kirby**.\n",
     "\n",
-    "## Course content\n",
-    "\n",
-    "As well as the self-study tutorial notebooks, three slide decks will be used. These can be downloaded from [here](https://github.com/coding-for-reproducible-research/CfRR_Courses/tree/main/individual_modules/introduction_to_machine_learning/slides).\n",
-    "\n",
     "## License info\n",
     "\n",
     "Most materials are licensed under MIT. The machine learning pipeline example is licensed under Apache 2.0."