Detailed Syllabus
| Week# | Date | Title | HW | MP | Grad Project |
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| 1 | Aug 27 | Lecture 1: Course outline
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| Aug 29 | Lecture 2: Probability Basics Overview, Probability and Hypothesis Testing, P-values , Fitting Distributions (KS test, KL divergence); Introducing Mini-project 1; Demostrating AV Simulator with Carla Slides | HW0 release HW0 solution |
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| 2 | Sep 3 | Lecture 3: In-class Activity 1 (Probability concepts, hypothesis testing, jupyter notebook) Slides | ICA1 ICA1 Solution |
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| Sep 5 | Lecture 4: Overview of unsupervised clustering algorithms: K means, Gaussian Mixture Model (GMM), Expectation Maximization (EM) and similar methods. Slides | MP1 Part1 Document MP1 Student Package |
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| Sep 6 (Friday) | Discussion session/open office hour on mini project 1 (optional discussion session, attendance is encouraged). 4 p.m./location TBD | ||||
| 3 | Sep 10 | Lecture 5: Regression and dimensionality reduction techniques: Distance Metrics, Principal Component Analysis (PCA), Factor analysis slides | |||
| Sep 12 | Lecture 6: Real-world application of dimensionality reduction slides | ||||
| 4 | Sep 17 | Lecture 7: In Class Activity 2 on dimensionality reduction | ICA2 ICA2 Solution |
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| Sep 19 | Lecture 8: Probabilistic Graph Models (PGMs) and applications: Conditional Independence and Naïve Bayes slides | HW1 release HW1 solution |
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| 5 | Sep 24 | Lecture 9: Bayesian Networks slides | MP1 Part2 Document MP1 DMV Data |
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| Sep 26 | Lecture 10: Bayesian networks/PGMs continue slides | ||||
| Sep 27 (Friday) | Discussion session/open office hour on Bayesian Network (optional discussion session, attendance is encouraged). 4 p.m./location TBD | ||||
| 6 | Oct 1 | Lecture 11: In Class Activity 3: Application of Bayesian Networks/PGMs to Health-care. | ICA3 ICA3 Solution |
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| Oct 3 | Lecture 12: Markov Models: Data driven methods for building Markov Models for large-scale computer system (NCSA’s Blue Waters) addressing performance and reliability; real example with data slides | MP1 Part3 Document MP1 Part 3 Data |
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| 7 | Oct 8 | Lecture 13: Hidden Markov Models (HMM) slides | |||
| Oct 10 | Lecture 14: In-Class Activity 4: Midterm review, sample problem-solving | ICA4 Practice Midterm ICA4 Practice Midterm solution Additional Practice Midterm |
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| Oct 11 (Friday) | Discussion session/open office hour: Midterm 4 p.m./location TBD | ||||
| 8 | Oct 15 | Lecture 15: MIDTERM EXAM – 60 minutes | |||
| Oct 17 | Lecture 16: In Class Problem Solving on HMM slides | ||||
| 9 | Oct 22 | Lecture 17: In Class Problem Solving on HMM Cont. slides | |||
| Oct 24 | Lecture 18: FGs: Introducing Factor Graphs, Belief Propagation and its Applications in FGs; Approximate solution methods slides | HW2 release HW2 Student Code HW2 solution |
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| Oct 25 (Friday) | Discussion session/open office hour on belief Propagation. (optional discussion session, attendance is encouraged). 4 p.m./location TBD | ||||
| 10 | Oct 29 | Lecture 19: FGs approximate solution methods Cont’d (Markov Chain Monte Carlo and Gibbs Sampling) slides | |||
| Oct 31 | Lecture 20: In Class Activity 6 on factor graphs | ICA6 ICA6 Solution |
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| 11 | Nov 5 | Lecture 21: Introduction to emergent LLMs role in new generation of inference models | |||
| Nov 7 | Lecture 22: Role of Neural Networks, LLM Architecture, and Attention Mechanism slides | ||||
| 12 | Nov 12 | Lecture 23: LLM Architecture and Training: Role of Transformers and RL, Mini Project 2 on LLMs Intro slides | |||
| Nov 14 | Lecture 24: Addressing Hallucinations: Retrieval Augmented Generation slides | ||||
| Nov 15 (Friday) | Discussion session/open office hour on mini project 2 and introduction to LLMs (optional discussion session, attendance is encouraged). 4 p.m./location TBD | MP2 Part1 Document MP2 Part 1 Student Code MP2 Part 1 Sentences |
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| 13 | Nov 19 | Lecture 25: In Class Activity 7 on LLMs | ICA7 ICA7 Solution |
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| Nov 21 | Lecture 26: LLM Model Training: Forward Backward Propagation and LLM Pre-training slides | ||||
| 14 | Nov 26 | No classes – Fall break | |||
| Nov 28 | No classes – Fall break | HW3 release HW3 solution |
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| 15 | Dec 3 | Lecture 27: LLM Prompting and Chain of Thoughts slides | MP2 Part2 Document MP2 Part 2 Student Code MP2 Part 2 RAG Data |
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| Dec 5 | Lecture 28: RAG and LLM Finetuning slides | MP2 Part3 Document MP2 Part 3 Student Code MP2 Part 3 XID Errors |
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| 16 | Dec 10 | Lecture 29: Review and Practice Final Exam | ICA8 ICA8 Solution |
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| Dec 12 | Reading day: No Class | ||||
| Dec 16 | FINAL EXAM |