01 - Course overview.ipynb

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    "# Foundations of Machine Learning\n",
    "## Course overview"
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    "### Learning objectives:\n",
    "\n",
    "- Build predictive models from training data\n",
    "- Correctly evaluate predictive models\n",
    "- Analyze and compare the performance of different models\n",
    "- Reason about the mathematical foundations of data mining techniques\n",
    "- Recognize when a predictive model is under/overfitting\n",
    "- Understand and exploit the bias-variance tradeoff\n",
    "- Combine the above with dimension-reduction techniques\n",
    "- Visualize and explore data using embeddings and clustering"
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    "### Lecturers:\n",
    "\n",
    "- Joaquin Vanschoren (j.vanschoren@tue.nl) MF 7.104a\n",
    "- Vlado Menkovski (v.menkovski@tue.nl) MF 7.097b\n",
    "- Anne Driemel (a.driemel@tue.nl) MF 7.073"
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    "### Contact hours:\n",
    "\n",
    "- Mondays, 10:45 - 12:30: Plenary Lectures (AUD 3)\n",
    "- Thursdays, 13:45 - 15:30: Tutorials and Feedback (AUD 3)\n",
    "- Thursdays, 15:45 - 17:30: Plenary Lectures (Flux 1.03)"
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    "### Course materials: \n",
    "  \n",
    "- Lecture materials (Notebooks+PDFs) on GitHub\n",
    "    - https://github.com/joaquinvanschoren/ML-course\n",
    "    - The README contains pointers to relevant books\n",
    "- See Canvas for:\n",
    "    - Syllabus\n",
    "    - Announcements, discussions\n",
    "    - Assignments, grades"
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    "### Evaluation:\n",
    "\n",
    "- No exam, only assignments (4 problem sets)\n",
    "- Preliminary(!) overview:\n",
    "\n",
    "    - 1: Linear Models, Model selection, Ensembles\n",
    "        - Released Feb 9 , Deadline Feb 23\n",
    "    - 2: Kernel method and Bayesian Inference\n",
    "        - Released Feb 23, Deadline Mar 16\n",
    "    - 3: Dimensionality reduction, Embeddings\n",
    "        - Released Mar 16, Deadline Mar 30\n",
    "    - 4: Deep learning\n",
    "        - Released Mar 30, Deadline Apr 13"
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    "- Work in teams of 2 students\n",
    "    - Free choice, rotated after **every** assignment\n",
    "\n",
    "- Passing grade: 6/10 over all assignments"
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