The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

• Attendance to at least 80% of the classes (it is allowed to miss maximum 2 times)

• Completion of mandatory milestones defined by the course instructors

• Delivery of a machine learning project based on the TensorFlow API and including the needed documentation.

Online Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Projects:

Check the Projects section to learn more about the projects.

This week you will...

• get a basic introduction to neural nets in order to get a first intuition in the underlying mechanisms

• get a first idea about possible projects you might want to work on throughout the course

Learning Resources

• Video (12 minutes)

• from Kaggle

• on Medium

Until next week you should...

• Watch the following videos:

  • (Fireship, 2:30 min)

  • (IBM Technology, 10 min)

This week you will...

• see how to improve the basic neural network for computer vision you learned last week.

• learn about what happens if your data is more complex; if the images are larger, or if the features are not always in the same place, and how to handle such issues.

Learning Resources

• (StatQuest Video, 6 min)

• (StatQuest, 7 min)

• (StatQuest, 7 min)

Until next week you should...

• Watch the following videos:

  • Theory:

    • (StatQuest with Josh Starmer, 15 min)

This week you will...

• get individual feedback on your project idea from one of the instructors

We will then assign your project team a time slot within the regular course time with one of the instructors.

Until next week you should...

• complete week 1, 2, and 3 of the course from DeepLearning.AI

• complete the tasks in to learn about data preparation for time series predictions

• If you want you additionally complete the tasks in this to do a time series prediction of the above prepared dataset.

This week you will...

• get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along.

This week you will...

• get an understanding for the importance of tokenization of a text when training a neural network for texts, for example, to do a sentiment analysis. Tokenization is the process of converting the text into numeric values, with a number representing a word or a character.

Before the first class you should ...

• register with to get a corresponding account.

This week you will...

• go deeper into using ConvNets with real-world data and a much larger dataset than those you've been using thus far.

This week you will...

• start digging into a variety of model formats that are used in training models to understand context in sequence. In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'.

• learn about using natural language processing (NLP) models for predictions. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, you'll build a text generator. It's trained with texts that mimic the style of master Yoda from Star Wars and can be used to produce sentences that sound similar those of Yoda.

Learning Resources

• Will be provided here soon.

Until next week you should...

• prepare questions for the instructor team on problems you have in your project or potential improvement ideas you are thinking of.

This week you will...

• get individual feedback on your project idea from one of the instructors

We will then assign your project team a time slot within the regular course time with one of the instructors.

Until next week you should...

• Watch the following videos:

  • (9 min)

  • (5 min)

This week you will...

• take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series.

• begin to teach neural networks to recognize and predict on time series.

• explore using Recurrent Neural networks (RNN) and Long Short Term Memory (LSTM) networks and see how useful they are to classify and predict on sequential data.

• add CNNs on top of Dnns and RNNs and put it together using a real world data series -- one which measures sunspot activity over hundreds of years,

Learning Resources

• Week 1 and 2 of the course

Until next week you should...

• complete your project work

• prepare your project presentation according to the instructions given

This week you will...

• get familiar wit transfer learning, a powerful technique to include the knowledge of models that were trained on large datasets and benefit from the features these models already learned in your own problem scenario.

• move beyond binary classification into categorical classification and the specific coding considerations for the corresponding models.

• present your key findings for the literature review considering you selected project

Learning Resources

• (8 min)

• on how to implement transfer learning with CNNs (12 min)

• to learn about multi-class classification

Until next week you should...

• Prepare questions for the instructor team on potential problems you see in your project

• Watch the following videos:

  • (9 min)

This week you will...

• present your project in the final presentations. :-)

After the Final Presentation you should...

• complete your project documentation and submit it according to the instructions given