This course covers popular advanced machine learning and deep learning concepts including Recommender Systems (RS) and Deep RS, Bayesian networks, Probabilistic Graphical Models, Natural Language Processing (NLP), LSTM (Long Short-Term Memory) neural networks, Generative Adversarial Networks (GANs), and advanced computer vision techniques such as U-Nets. Students will complete individual comprehensive projects in one of these topic areas and present their findings to the class in a seminar format.
TensorFlow / Keras is one of the most widely used frameworks for deep learning. The models, algorithms and techniques highlighted in this course are extensively used in industry.
Course Delivery: online | 7 weeks | 13 sessions
Course Credits: 3 units | 37.5 Seat Hours | 75 Total Hours
By the end of this course, you will be able to ...
- Build pipelines for machine learning and deep learning models
- Understand the TensorFlow / Keras environment and use them to build, test, and tune deep learning models
- Understand a variety of Computer Vision classification problems, and use common CNN (convolutional neural network) architectures to solve them
- Understand Sequence Modeling and its application to time series forecasting
- Understand the operation of RNN (Recurrent Neural Network), LSTM (Long Short Term Memory) and GRUs (Gated Recurrent Unit) cells and 1-dimensional CNNs and their application to Sequence Modeling problems.
Course Dates: Wednesday, January 20 – Wednesday, March 3, 2021 (7 weeks)
Class Times: Monday and Wednesday at 9:30–12:15pm (13 class sessions)
| Class | Date | Topics |
|---|---|---|
| - | Mon, Jan 18 | NO CLASS - MLK Day |
| 1 | Wed, Jan 20 | Text Classification using a Language Model |
| 2 | Mon, Jan 25 | Introduction to Artificial Neural Networks with Keras, part 1 |
| 3 | Wed, Jan 27 | Introduction to Artificial Neural Networks with Keras, part 2 |
| 4 | Mon, Feb 1 | Introduction to Artificial Neural Networks with Keras Part 3 |
| 5 | Wed, Feb 3 | Training Deep Neural Networks |
| 6 | Mon, Feb 8 | Custom Models and Training with TensorFlow |
| 7 | Wed, Feb 10 | Loading and Preprocessing Data with TensorFlow |
| 8 | Mon, Feb 15 | Deep Computer Vision Using Convolutional Neural Networks, Part 1 |
| 9 | Wed, Feb 17 | Deep Computer Vision Using Convolutional Neural Networks, Part 2 |
| 10 | Mon, Feb 22 | Deep Computer Vision Using Convolutional Neural Networks, Part 3 |
| 11 | Wed, Feb 24 | Sequence Modeling Part 1 |
| 12 | Mon, Mar 1 | Sequence Modeling Part 2 |
| 13 | Wed, Mar 3 | Sequence Modeling Part 3 |
Class Assignments are on Gradescope
- Extra Credit: Sentiment classification with NLP
- Fashion MNIST Model Training
- Train a deep neural network on the CIFAR-10 image dataset
- convolution code
- output size and number of parameters in neural network layers
- Sequence to sequence modeling (TBD)
If you have a disability that needs an accommodation such as extended time or a different format, please take advantage of our accommodations program, by filling out the intake form.
To pass this course you must meet the following requirements:
- Complete 4 of the 5 homework assignments with a grade of 70% or higher
- The Extra Credit Assignment can replace one missed assignment
- Actively participate in class and abide by the attendance policy
Any additional resources you may need (online books, etc.) can be found here. You can also find additional resources through the library linked below:
- Program Learning Outcomes - What you will achieve after finishing Make School, all courses are designed around these outcomes.
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- Course Credit Policy - Our policy for how you obtain credit for your courses
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- Student Handbook - Guidelines, policies, and resources for all Make School students