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Welcome to DSCI 571: Supervised Learning I! This course introduces fundamental concepts and techniques in supervised machine learning such as data splitting, cross-validation, generalization, overfitting, the fundamental trade-off, the golden rule, and data preprocessing. Additionally, we will explore popular machine learning algorithms, including decision trees, \(k\)-nearest neighbors, SVMs, naive Bayes, and linear models, using the scikit-learn framework.

Course learning outcomes#

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By the end of this course, you will be able to:

  • Describe supervised learning and its suitability for various tasks.

  • Explain key machine learning concepts such as classification, regression, overfitting, and the trade-off in model complexity.

  • Identify appropriate data preprocessing techniques for specific scenarios, provide reasons for their selection, and integrate them into machine learning pipelines.

  • Develop an intuitive understanding of common machine learning algorithms.

  • Build end-to-end supervised machine learning pipelines using Python and scikit-learn on real-world datasets.

Deliverables#

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The following deliverables will determine your course grade:

Assessment

Weight

Where to submit

Lab Assignment 1

12%

Gradescope

Lab Assignment 2

12%

Gradescope

Lab Assignment 3

12%

Gradescope

Lab Assignment 4

12%

Gradescope

iClicker participation

2%

iClicker Cloud

Quiz 1

25%

Canvas

Quiz 2

25%

Canvas

See Calendar for the due dates.

Teaching Team#

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Role

Name

Lecture Instructor

Varada Kolhatkar

Lab Instructor

Varada Kolhatkar

Teaching Assistant

Armin Saadat Boroujeni

Teaching Assistant

Daniel Ramandi

Teaching Assistant

Faeze Keshavarz

Teaching Assistant

Md Shahriar Rahim Siddiqui

Teaching Assistant

Negar Sadrzadeh

Teaching Assistant

Prajeet Bajpai

Lectures#

Format#

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This course follows a semi-flipped classroom format, where you will watch pre-recorded videos before class. In-class sessions will focus on demos, iClicker questions, Q&A, discussions, and worksheets. It’s optional but highly recommended to download the appropriate datasets provided below and put them under your local lectures/data directory, and run the lecture Jupyter notebooks on your own and experiment with the code.

Lecture schedule#

This course occurs during Block 2 in the 2023/24 school year.

Lecture

Topic

Assigned videos

Resources and optional readings

Course information

📹 Pre-watch: 1.0

1

Terminology, baselines, decision trees

📹 Pre-watch: 2.1, 2.2, 2.3, 2.4

2

ML fundamentals

📹 Pre-watch: 3.1, 3.2, 3.3, 3.4

An article by Pedro Domingos

3

\(k\)-NNs, SVM RBF

📹 Pre-watch: 4.1, 4.2, 4.3, 4.4, 5.1

4

Preprocessing, pipelines, column transformer

📹 Pre-watch: 5.2, 5.3, 5.4, 6.1

5

More preprocessing, text features

📹 Pre-watch: 6.2

6

Hyperparameter optimization, optimization bias

📹 Pre-watch: 8.1, 8.2

7

Naive Bayes

None

  • Conditional probability visualization
  • Naive Bayes chapter, Jurafsky and Martin

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    8

    Logistic Regression, multi-class classification

    📹 Pre-watch: 7.1, 7.2, 7.3

    Datasets#

    Here is the list of Kaggle datasets we’ll use in this class.

    If you want to be extra prepared, you may want to download these datasets in advance and save them under the lectures/data directory in your local copy of the repository.

    Labs#

    During labs, you will be given time to work on your own or in groups. There will be a lot of opportunity for discussion and getting help during lab sessions. (Usually I enjoy labs a lot. It’s also an opportunity for me to know you a bit better 🙂.)

    Installation#

    We are providing you with a conda environment file which is available here. You can download this file and create a conda environment for the course and activate it as follows.

    conda env create -f env-dsci-571.yaml
conda activate 571

    In order to use this environment in Jupyter, you will have to install nb_conda_kernels in the environment where you have installed Jupyter (typically the base environment). You will then be able to select this new environment in Jupyter. If you’re unable to see the environment in Jupyter, you might have to install the kernel manually. See the documentation here. For more details on this, refer to your 521 lecture 7.

    I’ve only attempted to install this environment file on a few machines, and you may encounter issues with certain packages from the yaml file when executing the commands above. This is not uncommon and may suggest that the specified package version is not yet available for your operating system via conda. When this occurs, you have a couple of options:

    1. Modify the local version of the yaml file to remove the line containing that package.

    2. Create the environment without that package.

    3. Activate the environment and install the package manually either with conda install or pip install in the environment.

    Note that this is not a complete list of the packages we’ll be using in the course and there might be a few packages you will be installing using conda install later in the course. But this is a good enough list to get you started.

    Course communication#

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    We are all here to support your learning and success in the course and the program. Here’s how our communication will work during the course.

    Clarifications on the lecture notes or lab questions#

    If there is any clarification on the lecture material or lab questions, I’ll open an issue in the course repository and tag you. I will also post a Slack message and tag you. It is your responsibility to read the messages whenever you are tagged. (I know that there are too many things for you to keep track of. You do not have to read all the messages but please make sure to carefully read the messages whenever you are tagged.)

    Questions on lecture material or labs#

    If you have questions about the lecture material or lab questions please post them on the course Slack channel rather than direct messaging me or the TAs. Here are the advantages of doing so:

    • You’ll get a quicker response.

    • Your classmates will benefit from the discussion.

    When you ask your question on the course channel, please avoid tagging the instructor unless it’s specific for the instructor (e.g., if you notice some mistake in the lecture notes). If you tag a specific person, other teaching team members or your colleagues are discouraged to respond. This decreases the response rate on the channel.

    Please use some consistent convention when you ask questions on Slack to facilitate easy search for others or future you. For example, if you want to ask a question on Exercise 3.2 from Lab 1, start your post with the label lab1-ex2.3. Or if you have a question on lecture 2 material, start your post with the label lecture2. Once the question is answered/solved, you can add “(solved)” tag before the label (e.g., (solved) lab1-ex2.3. Do not delete your post even if you figure out the answer on your own. The question and the discussion can still be beneficial to others.

    Reference Material#

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    Books#

    Online courses#

    Misc#

    Policies#

    Please see the general MDS policies.

    Enjoy your learning journey in DSCI 571: Supervised Learning I!