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Syllabus: syllabus.pdf

Course Material (Slides and Videos): Dropbox

Schedule your one-on-one meeting with Dr. Farbin. Schedule Meeting

Course Plan:

  1. Introduction: Lecture 1 (W- 1/18): Why HEP and DL? Slides Video
  2. Technical Background:
    1. Lecture 2 (F- 1/20): Introduction (continued) Slides Video
    2. Labs 1 & 2 (M- 1/22): Lab-1 Lab-2
    3. Lecture 3 (W- 1/25): Unix, python, numpy, and all that... Slides Video
  3. Math Background:
    1. Lecture 4 (F- 1/27): Linear Algebra Slides Example Video
    2. Lecture 5 (M- 1/30): Numpy Slides Examples Video
    3. Lecture 6 (W- 2/1- Census date): Probability Theory  Notes Video
    4. Lecture 7 / Lab 3 (F- 2/3):  LArTPC Dataset Slides Video
    5. Lecture 8 (M- 2/6): Probability Theory (continued)
    6. Lecture 9 (W- 2/8): Model Building I Notes
  4. Machine Learning:
    1. Lecture 10 (F- 2/10): Model Building II Notes
    2. Lecture 11 (M- 2/13): Model Building III Notes
    3. Lecture 12 (W- 2/15): Lab 3 Due. Neural Networks. Lab 4 (Supersymmetry Dataset) given out. Notes
  5. Physics Background
    1. Lecture 13 (F- 2/17) History of Particles Slides
    2. Lecture 14 (M- 2/20): Why HEP? (Standard Model) Slides (lectures 14-15)
    3. Lecture 15: (W- 2/22): Hierarchy Problem and Supersymmetry
  6. Event Classification
    1. Lecture 16 (F-2/24): Kinematics I Notes (Lectures 16-18) 
    2. Lecture 17 (M- 2/27): Kinematics II. Lab 4 Due. Exam 1 Handed out. 
    3. Lab  (W- 3/1):  Searching for Particles at the LHC. HiggsML Challenge. 
  7. Particle Identification
    1. (F- 3/3) : Canceled 
    2. Lecture 18 (M- 3/6) : Higgs to tautau and HiggsML Slides Video
    3. Lecture 20 (W- 3/8) : Exam 1 / HiggsML Q & A
  8. Unsupervised Learning
    1. Lecture 21 (F- 3/10): Exam 1 Due. Convolutional Neural Networks. Lab 5- LArTPC DNN
    2. Lecture 22 : Clustering, Transfer Learning, Auto-encoders, Noise-suppression
    3. Lab : LCD Dataset
  9. Spring Break (M- 3/13 - F 3/17)
  10. Regression
    1. Lecture  (M- 3/20): Calorimetry
    2. Lecture  (M- 3/22): Regression
    3. Lab  (M- 3/24): Top Matrix Element Dataset
  11. Projects I
    1. (M- 3/27)
    2. (W- 3/29)
    3. (F- 3/31)
  12. Special Topics Lectures
    1. (M- 4/3)
    2. (W- 4/5)
    3. (F- 4/7)
  13. Presentations I
    1. (M- 4/10)
    2. (W- 4/13)
    3. (F- 4/15)
  14. Projects II
    1. (M- 4/17)
    2. (W- 4/19)
    3. (F- 4/21)
  15. Special Topics Lectures
    1.  (M- 4/24)
    2. (W- 4/26)
    3. (F- 4/28)
  16. Presentations II
    1.  (M- 5/1)
    2. (W- 5/3)
    3. (F- 5/5- Last day of classes)
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