MC886 / MO444 - Machine Learning and Pattern Recognition

Time and Place: Tuesdays and Thursdays, from 19h to 21h. Living room CB05.

Waiters The service hours will always be provided after classes by the teacher or scheduled in advance by email with PED Alceu Bissoto, alceubissoto@gmail.com.

Course Program: Introduction to Machine Learning, Linear Regression, Logistic Regression, PCA and LDA, k-means, Neural Network, Deep Learning, SVM and Kernels, Boosting and Random Forest.

Programming language: The programming language used in the course is free, as long as it is compatible and justified in the context of the problem. Recommendation: Python.

Assessment: The evaluation will be based on active classroom participation, proposed activities, and practical projects, being:

• At the end of the class, the student must send via Moodle what was the point of biggest doubt of the class (D), a succinct question. If you have no doubts (seriously?), The student should highlight the point that he 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 Moodle.
• Um final project PF to be carried out in groups:
  • The student will only be able to do the final PF project if he has delivered the four tasks T1, T2, T3 and T4.
  • Groups must have 4 to 5 students, necessarily.
  • The code and the report must be delivered via Moodle, 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 class by the group on the scheduled date.
• The final average, M, will be calculated as: M = 0,05 x D (extra) + 0,15 x T1 + 0,2 x T2 + 0,15 x T3 + 0,1 x T4 + 0,4 x PF
  • For undergraduate students, the following rule will apply:
    • Approved: if M ≥ 5.0
    • Failed: if M <5.0
  • For graduate students, the final grade will be assigned as follows:
    • A: if M ≥ 8.5
    • B: if 7.0 ≤ M <8.5
    • C: if 5.0 ≤ M <7.0
    • D: if M <5.0

Minimum Frequency: The frequency must be greater than or equal to 75% for approval.

Submission of Activities: All course activities must be submitted through the system Moodle in the corresponding area of ​​the discipline.

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

• Biggest Doubt (D): Until 15 pm the next day after class.
• Task 1 (T1): 30/08/2018
• Task 2 (T2): 25/09/2018
• Task 3 (T3): 23/10/2018
• Task 4 (T4): 08/11/2018
• Final Project (PF):
  • Proposal submission (theme and database): 06/09/2018
  • Baseline submission: 11/10/2018
  • Presentation (videos of up to 4 minutes): 27-29 / 11/2018. Examples.
  • PF submission (report and code): 06/12/2018

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 and TensorFlow”, Aurélien Géron, 2017.
• "Pattern Recognition and Machine Learning", Christopher M. Bishop, 2006.
• “Machine Learning: A Probabilistic Perspective,” Kevin P. Murphy, 2012.
• "Pattern Classification", David G. Stork, Peter E. Hart, and Richard O. Duda, 2000.
• "Deep Learning", Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016.