diff --git a/.gitignore b/.gitignore index f780d5761d..be030005fa 100644 --- a/.gitignore +++ b/.gitignore @@ -5,12 +5,17 @@ dist +#pythion virtual envs +venv* + # User-specific files *.rsuser *.suo *.user *.userosscache *.sln.docstates +#draw.io backups +*.bkp # User-specific files (MonoDevelop/Xamarin Studio) *.userprefs diff --git a/1-Introduction/1-intro-to-ML/DanNotes.txt b/1-Introduction/1-intro-to-ML/DanNotes.txt new file mode 100644 index 0000000000..4323963716 --- /dev/null +++ b/1-Introduction/1-intro-to-ML/DanNotes.txt @@ -0,0 +1,38 @@ +How are things learned? + Memorization + Accumulation of facts + Limited by: + Time to observe facts + Memory to observe facts + ---------- + This is "declarative knowledge" - based on statements of truth + ---------- + Generalization + Deduce new facts from old facts + Limited by: + Accuracy of the dedeuction process + Essentially a predictive activity + Assumes that the past predicts the future. + ---------- + This is "imperative knowledge" + ---------- + + +Basic paradigm: + - provide a set of - seen, observed - training data + - decide on a characteristic of that training data as representative for the issue + - infer something (a rule?) about the process that has generated that data + - use inference to make predictions about previously unseen data + - confirm inference using a set of test data + +A choice might have to be made between "Will I have false negatives or false positives allowed by my rules" and it would depend on what side is the risk higher. + +Issues of concern when learning models: + Leaned models will depend on : + - distance metric between examples + - choice of features vectors + - constraints of complexity model + - specified or unknown number of clusters + - complexity of separating surface + - need to acoid overfitting problems like "each example is its own cluster" + \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/Relationship-AI-DataScience.drawio b/1-Introduction/1-intro-to-ML/Relationship-AI-DataScience.drawio new file mode 100644 index 0000000000..31e17f2a15 --- /dev/null +++ b/1-Introduction/1-intro-to-ML/Relationship-AI-DataScience.drawio @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/1-Introduction/2-history-of-ML/HistoryOfML.drawio b/1-Introduction/2-history-of-ML/HistoryOfML.drawio new file mode 100644 index 0000000000..29e58b4bc7 --- /dev/null +++ b/1-Introduction/2-history-of-ML/HistoryOfML.drawio @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/1-Introduction/2-history-of-ML/ML-History.json b/1-Introduction/2-history-of-ML/ML-History.json new file mode 100644 index 0000000000..32e8f38cf7 --- /dev/null +++ b/1-Introduction/2-history-of-ML/ML-History.json @@ -0,0 +1,142 @@ +{ + "title": { + "media": { + "url": "https://en.wikipedia.org/wiki/File:Alan_Turing_(1912-1954)_in_1936_at_Princeton_University_(cropped).jpg", + "caption": "Turing in 1936.", + "credit": "wikipedia/wikipedia" + }, + "text": { + "headline": "History of machine learning
1950 - 2011", + "text": "

The history of artificial intelligence (AI) as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important algorithmic, statistical, mathematical, computational and technical discoveries predated and overlapped this era. In fact, people have been thinking about these questions for hundreds of years: this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'.

" + } + }, + "events": [ + { + "media": { + "url": "https://s.abcnews.com/images/Entertainment/whitney-cissy-dionne-gty-er-180711_hpEmbed_21x16_992.jpg", + "caption": "Houston's with her mother and Gospel singer, Cissy Houston and cousin Dionne Warwick.", + "credit": "Cissy Houston photo:Tom Marcello
Dionne Warwick: CBS Television via Wikimedia Commons" + }, + "start_date": { + "year": "1950" + }, + "text": { + "headline": "Machines that think", + "text": "

Alan Turing, a truly remarkable person who was voted by the public in 2019 as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think.' He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the Turing Test, which you will explore in our NLP lessons.

" + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1956" + }, + "text": { + "headline": "Dartmouth Summer Research Project", + "text": "The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field, and it was here that the term 'artificial intelligence' was coined (source)." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1956" + }, + "end_date": { + "year": "1974" + }, + "text": { + "headline": "The golden years of AI", + "text": "From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that 'Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved.' (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1974" + }, + "end_date": { + "year": "1980" + }, + "text": { + "headline": "The AI Winter", + "text": "By the mid 1970s, it had become apparent that the complexity of making 'intelligent machines' had been understated and that its promise, given the available compute power, had been overblown. Funding dried up and confidence in the field slowed." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1980" + }, + "end_date": { + "year": "1990" + }, + "text": { + "headline": "The years of the Expert systems", + "text": "As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. Expert systems were among the first truly successful forms of artificial intelligence (AI) software." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1987" + }, + "end_date": { + "year": "1993" + }, + "text": { + "headline": "The AI Chill", + "text": "The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "1993" + }, + "end_date": { + "year": "2011" + }, + "text": { + "headline": "The ML Era", + "text": "This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline." + } + }, + { + "media": { + "url": "https://youtu.be/fSrO91XO1Ck", + "caption": "", + "credit": "Unidisc Music" + }, + "start_date": { + "year": "2020" + }, + "text": { + "headline": "Now", + "text": "Today machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, 'Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses' (source)." + } + } + ] +} diff --git a/1-Introduction/2-history-of-ML/index.html b/1-Introduction/2-history-of-ML/index.html new file mode 100644 index 0000000000..82586669bc --- /dev/null +++ b/1-Introduction/2-history-of-ML/index.html @@ -0,0 +1,21 @@ + + + + + + + + + + + +
+ + \ No newline at end of file diff --git a/1-Introduction/3-fairness/DanNotes.txt.bak b/1-Introduction/3-fairness/DanNotes.txt.bak new file mode 100644 index 0000000000..e69de29bb2 diff --git a/1-Introduction/4-techniques-of-ML/DanNotes.txt b/1-Introduction/4-techniques-of-ML/DanNotes.txt new file mode 100644 index 0000000000..124166c9db --- /dev/null +++ b/1-Introduction/4-techniques-of-ML/DanNotes.txt @@ -0,0 +1,14 @@ +- Decide is AI is the right approcahc for your problem + - if the problem can be solved with a well defined set of rules -> not AI + - plenty of data with useful information about your problem -> AI +- Collect and prepare your data + - cleanup, format, eliminate rows or fields + - choose features that you will use as input for predictions (suh as medical history) + - choose what you will predict - probability for a disease + - split into training data and test data, say 80% to 20% +- Train your model + - chose algorithms or use them all. +- Evaluate your model +- Tuning the model's hyperparameters +- Testing the trained model in the real-world + diff --git a/2-Regression/1-Tools/DanNotes.txt b/2-Regression/1-Tools/DanNotes.txt new file mode 100644 index 0000000000..2603341c48 --- /dev/null +++ b/2-Regression/1-Tools/DanNotes.txt @@ -0,0 +1,8 @@ +python -m venv sklearn-env +sklearn-env\Scripts\activate # activate +pip install -U scikit-learn + + +python -m pip show scikit-learn # show scikit-learn version and location +python -m pip freeze # show all installed packages in the environment +python -c "import sklearn; sklearn.show_versions()" \ No newline at end of file diff --git a/2-Regression/1-Tools/assignment.md b/2-Regression/1-Tools/assignment.md index de37856c51..39d4d46ebc 100644 --- a/2-Regression/1-Tools/assignment.md +++ b/2-Regression/1-Tools/assignment.md @@ -6,6 +6,9 @@ Take a look at the [Linnerud dataset](https://scikit-learn.org/stable/modules/ge In your own words, describe how to create a Regression model that would plot the relationship between the waistline and how many situps are accomplished. Do the same for the other datapoints in this dataset. +I would load the data in the column at index 1 (situps) as a numeric predictive value and the column at index 1 (waistline) as predictive target. I would split the sets in 2/3rds for training and 1/3rd for test. I would plot the resulkts of predictions against test values to confirm the corelation between situps and waistline - can the number of sitpus predict the waistline of a person. + + ## Rubric | Criteria | Exemplary | Adequate | Needs Improvement | diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb index e69de29bb2..2918ef4096 100644 --- a/2-Regression/1-Tools/notebook.ipynb +++ b/2-Regression/1-Tools/notebook.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# My first notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, world from python notebook\n" + ] + } + ], + "source": [ + "print(\"Hello, world from python notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(442, 10)\n", + "[ 0.03807591 0.05068012 0.06169621 0.02187239 -0.0442235 -0.03482076\n", + " -0.04340085 -0.00259226 0.01990749 -0.01764613]\n", + "(442,)\n", + "(442, 1)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model, model_selection\n", + "\n", + "#load diabetes dataset\n", + "X, Y = datasets.load_diabetes(return_X_y=True)\n", + "\n", + "#print the shape of the data and the first row\n", + "print(X.shape)\n", + "print(X[0])\n", + "\n", + "\n", + "#Extract the column at index 2\n", + "X = X[:, 2]\n", + "print(X.shape)\n", + "X = X.reshape((-1,1))\n", + "print(X.shape)\n", + "\n", + "#split the model into training and testing data\n", + "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y, test_size=0.33)\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train,Y_train)\n", + "\n", + "\n", + "#predict using our test data\n", + "Y_pred= model.predict(X_test)\n", + "\n", + "\n", + "#how good are our predictions ? let's use mathlib to find out.\n", + "\n", + "#create a scatter plot\n", + "plt.scatter(X_test, Y_test, color='black')\n", + "\n", + "# plot the predictions\n", + "plt.plot(X_test,Y_pred, color='blue', linewidth=3)\n", + "\n", + "#add labels and subtitles\n", + "plt.xlabel('Scaled BMIs')\n", + "plt.ylabel('Disease Progression')\n", + "plt.title('A Graph Plot Showing Diabetes Progression Against BMI')\n", + "\n", + "#draw the plot\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try another column.Available columns are: \n", + "\n", + " age age in years\n", + "\n", + " sex\n", + "\n", + " bmi body mass index\n", + "\n", + " bp average blood pressure\n", + "\n", + " s1 tc, total serum cholesterol\n", + "\n", + " s2 ldl, low-density lipoproteins\n", + "\n", + " s3 hdl, high-density lipoproteins\n", + "\n", + " s4 tch, total cholesterol / HDL\n", + "\n", + " s5 ltg, possibly log of serum triglycerides level\n", + "\n", + " s6 glu, blood sugar level\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(442, 10)\n", + "[ 0.03807591 0.05068012 0.06169621 0.02187239 -0.0442235 -0.03482076\n", + " -0.04340085 -0.00259226 0.01990749 -0.01764613]\n", + "(442,)\n", + "(442, 1)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model, model_selection\n", + "\n", + "#load diabetes dataset\n", + "X, Y = datasets.load_diabetes(return_X_y=True)\n", + "\n", + "#print the shape of the data and the first row\n", + "print(X.shape)\n", + "print(X[0])\n", + "\n", + "\n", + "#Extract the column at index 2\n", + "X = X[:,9]\n", + "print(X.shape)\n", + "X = X.reshape((-1,1))\n", + "print(X.shape)\n", + "\n", + "#split the model into training and testing data\n", + "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y, test_size=0.33)\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train,Y_train)\n", + "\n", + "\n", + "#predict using our test data\n", + "Y_pred= model.predict(X_test)\n", + "\n", + "\n", + "#how good are our predictions ? let's use mathlib to find out.\n", + "\n", + "#create a scatter plot\n", + "plt.scatter(X_test, Y_test, color='black')\n", + "\n", + "# plot the predictions\n", + "plt.plot(X_test,Y_pred, color='blue', linewidth=3)\n", + "\n", + "#add labels and subtitles\n", + "plt.xlabel('Sugarlevel')\n", + "plt.ylabel('Disease Progression')\n", + "plt.title('A Graph Plot Showing Diabetes Progression Against sugar level')\n", + "\n", + "#draw the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20, 3)\n", + "[ 5. 162. 60.]\n", + "(20, 3)\n", + "[191. 36. 50.]\n", + "(20,)\n", + "(20, 1)\n", + "(20,)\n", + "(20, 1)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model, model_selection\n", + "\n", + "#load diabetes dataset\n", + "X, Y = datasets.load_linnerud(return_X_y=True)\n", + "\n", + "#print the shape of the data and the first row\n", + "print(X.shape)\n", + "print(X[0])\n", + "\n", + "\n", + "print(Y.shape)\n", + "print(Y[0])\n", + "\n", + "\n", + "\n", + "#Extract the phys column at index 1\n", + "X = X[:,1]\n", + "print(X.shape)\n", + "X = X.reshape((-1,1))\n", + "print(X.shape)\n", + "\n", + "\n", + "Y = Y[:,1]\n", + "print(Y.shape)\n", + "Y = Y.reshape((-1,1))\n", + "print(Y.shape)\n", + "\n", + "#split the model into training and testing data\n", + "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y, test_size=0.33)\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X_train,Y_train)\n", + "\n", + "\n", + "#predict using our test data\n", + "Y_pred= model.predict(X_test)\n", + "\n", + "\n", + "#how good are our predictions ? let's use mathlib to find out.\n", + "\n", + "#create a scatter plot\n", + "plt.scatter(X_test, Y_test, color='black')\n", + "\n", + "# plot the predictions\n", + "plt.plot(X_test,Y_pred, color='blue', linewidth=3)\n", + "\n", + "#add labels and subtitles\n", + "plt.xlabel('Waistline(inches')\n", + "plt.ylabel('Number of situps')\n", + "plt.title('A graph plot ahowing number of situps against waistline')\n", + "\n", + "#draw the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-ml", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}