All posts by Han Gong

Module 10: Deep Learning

Module
  1. Prepare (due M 4/4)
    1. Content below
    2. Sakai quizzes
  2. Peer Instructions – See on the class forum
  3. Homework (due Su 4/10)
  4. There are no worked examples

Content

10 Deep Learning

  1. Neural Networks and Applications (16 min.)
  2. Forward Propagation (10 min.)
  3. Gradient Descent (14 min.)
  4. Back Propagation (11 min.)
  5. Convolutional Neural Network (15 min.)
  6. Introducing Pytorch (23 min.)

Optional Supplements

The deep learning book is available free online and is authored by some of the leading experts in machine learning with deep artificial neural networks. It is very detailed and in-depth and is purely for those who are interested in learning more about deep learning theory now or in the future; you do not need to read the book for this course.

Unlike most other libraries for this course, Pytorch is not included in the basic Anaconda installation. To use Pytorch, we suggest you choose one of two options.

  • Install Pytorch locally (for free). You can see the directions on the website: Select the stable build, your operating system, Conda (for Anaconda), Python, and CPU to see install directions for your particular setup. (CUDA is used to support hardware acceleration with NVIDIA graphics cards and is not necessary for this course).
  • Use Pytorch in a Jupyter notebook in the cloud (also for free). The easiest way to do this if you have a Google account is with a Google colab notebook; Pytorch will already be available to you in this cloud environment.

You can find the official Pytorch documentation here. Of particular note are the Pytorch tutorials, including Pytorch recipes which serve as small examples of common tasks.

Module 09: Prediction & Supervised Machine Learning

Module
  1. Prepare (due M 3/21)
    1. Content below
    2. Sakai quizzes
  2. Peer Instructions – See on the class forum
  3. Homework (due Su 3/27)
  4. Worked Examples

Content

9.A Predictive Modeling and Regression

  1. Ordinary Linear Regression and Intro Scikit-Learn (21 min.)
  2. Nonlinear Regression and Scikit-Learn Preprocessing (13 min.)
  3. Binary Classification with Logistic Regression (22 min.)

9.B Machine Learning and Classification

  1. Naïve Bayes and Text Classification (20 min.) – The video has a type on slide 10, see the pdf of the slides in Box for the fix.
  2. K-Nearest Neighbors and Training/Testing (31 min.)

Optional Supplements

Chapter 5 Machine Learning from the Python Data Science Handbook provides a very nice treatment of many of the topics from the above videos and more. If you are new to machine learning, we highly recommend that you read sections 5.1 “What is Machine Learning” through 5.4 “Feature Engineering” after completing the videos. After that, you can optionally read any of the In-Depth sections about specific algorithms for prediction.

In addition, the scikit-learn documentation itself provides several resources for working with the library:

Module 06: Combining Data

Module
  1. Prepare (due M 2/14)
    1. Content below
    2. Sakai quizzes
  2. Peer Instructions — See on the class forum
  3. Homework (due Su 2/20)

Content

6.A – Summarizing Data

  1. Read Section 3.8 Aggregating and Grouping from Python Data Science Handbook.
  2. Read Section 3.9 Pivot Tables from Python Data Science Handbook.

6.B – Merging Data

  1. Record Linkage (8 min.)
  2. Read Section 3.6 Concat and Append from Python Data Science Handbook. Please note that the join_axes optional parameter mentioned in this section has been deprecated from the Pandas library, you can skip over the details on this parameter.
  3. Read Section 3.7 Merge and Join from Python Data Science Handbook
  4. Fuzzy Matching (21 min.)

Optional Supplements