TA: Vamshi Nagabandi, ITE 353, nvamshi1@umbc.edu, Office hours: Monday 12PM - 2PM, Wednesday 12PM - 2PM
The weights on the various items is as follows:
Hard copy is required for all assignments. If you are not on campus for the start of class (what?!?!) and want to email your assignment to me or the TA to establish that it was done on time, that's OK. However, you must hand in hard copy before the assignment will be graded.
The project is meant to give students deeper exposure to some topic in machine learning than they would get from the lectures, readings, and discussions alone. Those projects that are most successful often blend the student's own research with machine learning, e.g. by applying machine learning techniques to a problem in some other area, or by bringing an insight from some other area to a problem in machine learning. However, projects need not involve ties with ongoing research. Many good projects in the past have investigated the application of existing algorithms to a domain/dataset of interest to the student, such as Texas Hold'em, the stock market, sporting events, and so on. Students can come up with their own project ideas or they can come see me and we'll brainstorm project ideas. information.
Projects may be done by individuals or teams of two people. However, teams of two will be expected to do significantly more work than what is expected of an individual project. More information on projects can be found here.
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1 | Tue 29 Jan | Course overview, decision trees | Chapter 1 of CIML |
2 | Thu 31 Jan | Decision trees, over-/under-fitting, advice | |
3 | Tue 5 Feb | Geometry, k-nearest neighbor | Chapter 2 of CIML |
4 | Thu 7 Feb | k-nearest neighbor, k-means clustering | HW 1 assigned |
5 | Tue 12 Feb | Perceptron | Chapter 3 of CIML |
6 | Thu 14 feb | Perceptron | HW 1 due HW 2 assigned |
7 | Tue 19 Feb | Logistic regression | Slides, thanks to Tom Dietterich |
8 | Thu 21 Feb | Logistic regression, practical issues | Chapter 4 of CIML HW 2 due HW 3 assigned |
9 | Tue 26 Feb | Practical issues | |
10 | Thu 28 Feb | Probabilistic learning | Chapter 7 of CIML Bayes Nets chapter HW 3 due HW 4 assigned |
11 | Tue 5 Mar | Probabilistic learning | |
12 | Thu 7 Mar | Probabilistic learning | Project proposal due |
13 | Tue 12 Mar | Probabilistic learning | |
14 | Thu 14 Mar | Reinforcement learning | Parts of chapters 3, 4, 5, and 6 of the
RL
book HW 4 due HW 5 assigned |
Tue 19 Mar | Spring Break | ||
Thu 21 Mar | Spring Break | ||
15 | Tue 26 Mar | Midterm Review on content of classes 1 - 13 | |
16 | Thu 28 Mar | Midterm Exam on content of classes 1 - 13 | |
17 | Tue 2 Apr | Reinforcement learning | |
18 | Thu 4 Apr | Reinforcement learning | |
19 | Tue 9 Apr | Neural networks | Chapter 11 of CIML HW 5 due HW 6 assigned |
20 | Thu 11 Apr | Neural networks and deep learning | |
21 | Tue 16 Apr | Deep learning | |
22 | Thu 18 Apr | Linear methods | Chapter 6 of CIML HW 6 due HW 7 assigned |
23 | Tue 23 Apr | Linear methods | |
24 | Thu 25 Apr | Kernel methods | HW 7 due HW 8 assigned |
25 | Tue 30 Apr | Kernel methods | |
26 | Thu 2 May | Ensemble learning | Chapter 11 of CIML HW 8 due HW 9 assigned |
27 | Tue 7 May | Ensemble learning | |
28 | Thu 9 May | Project presentations | HW 9 due Henry; Ferry; Lin; Richards; Genega; McCarter; Milani; Dunstan; Bradford; Bowen; Mann; Carver; Arminger; Hwang; Kempton; Gorelik; Little |
29 | Tue 14 May | Project presentations | Patel; Sprehe; Weiss; Korb; Kareem; Singh; Vance; Coleman; Mukhin; Ochoa; Dudley; Huganir; Gebremariam; Nettie; Nehman; Byerly; Miller |
Thu 16 May | Final Exam 10:30AM - 12:30PM | ||
Thu 23 May | Final project writeup due |