#align(center)[#text(size: 20pt)[Course Syllabus]]
#table(
  columns: (0.2fr, 0.8fr),
  [Course], [CISC 7026 Fall 2024],
  [Time], [19:00-22:00, Mondays],
  [Location], [Room E6-1102C],
  [Description], [This course introduces the theory and application of deep neural networks],
  [Instructor], [
    <NAME> \<smorad at um.edu.mo\>
  ],
  [Office Hours], [11:00-12:00 Mondays and Tuesdays],
  [Teaching Assistants], [TBD],
  [Grading], [
    - Assignments: 70%
    - Quizzes: 20%
    - Participation: 10%
  ],
  [Late Work Policy], [
    - -25% 0-1 days late
    - -50% 1-2 days late
    - -75% 2-3 days late
    - -100% 3+ days late
  ],
  [Prerequisites], [
    - Linear Algebra
    - Multivariable Calculus
    - Programming in Python
  ],
  [Preliminary Lecture Schedule], [
    - Week 1 (08.19): No Lecture (visa issues)
    - Week 2 (08.26): Introduction to the Course
    - Week 3 (09.02): Linear Regression (D2L 3.1, 3.6)
    - Week 4 (09.09): Neural Networks (D2L 5.1, 5.2, 6.1)
    - Week 5 (09.16): Backpropagation and Optimization (D2L 5.3, 12.1, 12.3-12.5)
    - Week 6 (09.23): Classification (D2L 4.1, 4.2, 4.4)
    - Week 7 (09.23): Training Tricks (D2L 5.1-5.5, 6.1-6.3, 12.1-12.10)
    - Week 8 (09.30): Convolutional Neural Networks (D2L 7)
    - Week 9 (10.14): Recurrent Neural Networks (D2L 9-10)
    - Week 10 (10.21): Graph Neural Networks
    - Week 11 (10.07): Autoencoders and Generative Models
    - Week 12 (10.28): Attention and Transformers (D2L 11.1-11.7)
    - Week 13 (11.04): Foundation Models (D2L 11.8-11.9)
    - Week 14 (11.11): Reinforcement Learning (D2L 17)
  ],
  [Preliminary Assignment Schedule], [
    - Week 3-4 (09.02 - 09.09): Linear Regression
    - Week 4-6 (09.09 - 09.23): Neural Networks and Backpropagation
    - Week 6-8 (09.23 - 10.07): MLP Regression
    - Week 8-10 (10.07 - 10.21): Convolutional MNIST Classification
    - Week 10-12 (10.21 - 11.04): LSTM Weather Prediction
    - Week 12-14 (11.04 - 11.18): Transformer IMDB Sentiment Analysis

  ]
)