MC886 - Machine Learning

Computing Institute (IC / Unicamp)
Prof. Sandra Avila (sandra@ic.unicamp.br)

 

Time and Place: Mondays and Wednesdays, from 19pm to 21pm. Classes will take place via Google Meet, with some classes being held synchronously and others asynchronously. For all synchronous classes, the videos of the classes will be made available later. Classes will start on September 16st / 2020.

Waiters The opening hours will be provided by the teacher and PEDs Giovanna Antonieti, Rosa Paccotacya Yanque and Vanessa Sidrim in attendance classes or by Slack (ml-unicamp-2020.slack.com).

Course Program: Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Linear Regression, Logistic Regression, Neural Networks, PCA and LDA, K-means, Deep Learning, SVM, Random Forest and Ensemble Learning

Programming language: The programming language used in the course is Python.

Assessment: The evaluation will be based on the proposed activities and practical projects, being:

• At the end of the class, the student must send via Google Classroom what was the point of biggest doubt of the class (D), a succinct question. If you have no doubts (really? Really?), The student should highlight the point that she found most interesting. Answers like “I had no doubts” or blank answers will not be accepted.
• Quatro double tasks, T1, T2, T3 and T4. The code and the report must be delivered via Google Classroom.
• Um final project PF to be carried out in groups:
  • The (O) student (o) will only be able to do the final PF project if she has delivered the four tasks T1, T2, T3 and T4.
  • The groups must have 3 students (the), necessarily.
  • The code and the report must be delivered via Google Classroom, and the report must present an explanation of the technique implemented, illustrations of the results, and a discussion of the results obtained in the format of a scientific article, in the model suggested by the teacher.
  • The project must be presented (in a 4-minute video format), by the group, on the scheduled date.
• The final average, M, will be calculated as: M = 0,1 x T1 + 0,2 x T2 + 0,15 x T3 + 0,1 x T4 + 0,4 x PF + 0,05 x D
  • Approved: if M ≥ 5.0
  • Failed: if M <5.0

Submission of Activities: All course activities must be submitted via Google Classroom.

Evaluation Delivery Dates: The dates below are subject to change.

• Biggest Doubt (D): Until 12:XNUMX, two days after class.
• Task 1 (T1): 05/10/2020
• Task 2 (T2): 02/11/2020
• Task 3 (T3): 16/11/2020
• Task 4 (T4): 14/12/2020
• Final Project (PF):
  • Proposal submission (theme and database): 12/10/2020
  • Baseline submission (first result): 30/11/2020
  • Presentation (videos of up to 4 minutes): 11-13 / 01/2021 Examples 2017.2 Examples 2018.2 Examples 2019.2
  • PF submission (report and code): 11/01/2021

Remarks:

• There will be no tests or exam for this discipline.
• Any attempted fraud in the activities of the discipline will imply a final average M = 0 (zero) for all persons involved, without prejudice to other sanctions.

References: The teacher will not follow a specific textbook, however, the following books cover what will be seen in class:

• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow", A. Géron, 2019.
• "Pattern Recognition and Machine Learning", CM Bishop, 2006.
• “Pattern Classification”, DG Stork, PE Hart, and RO Duda, 2000.
• "Deep Learning", I. Goodfellow, Y. Bengio, & A. Courville, 2016.
• “Dive into Deep Learning”, M. Gardner, M. Drummy, J. Quinn, J. McEachen, & M. Fullan, 2019.