- **Semester:** Fall 2025
- **Course Prefix/Number:** MAP6905
- **Course Title:** Directed Study: Advanced Topics in Deep Learning
- **Course Credit Hours:** 3.0
- **Class Meeting Time:** Weekly, by appointment
- **Instructor:** Prof. Achraf Cohen
- Mathematics and Statistics
- Building 4, Room 337B (Main Campus)
- Email: acohen@uwf.edu :::
## Course Description
This directed study investigates the advanced topics in Deep Learning for Artificial Intelligence. Topics include:
- Matrix theory for understanding and improving optimization algorithms (vector spaces, ranks, block operations, decompositions)
- Tensor factorization techniques relevant to modern architectures
- Stochastic gradient descent (SGD) and adaptive optimizers
- Stability of large-scale training (conditioning, vanishing/exploding gradients)
- Second-order optimization methods (natural gradient, quasi-Newton)
- Model compression strategies: quantization, pruning, distillation, low-rank approximations
- Scaling laws, training dynamics, and hardware-aware optimization
- Evaluation metrics beyond accuracy: FLOPs, latency, memory footprint, energy cost
Students will develop the mathematical and computational background to contribute to state-of-the-art research in model compression and efficient large-scale AI.
## Topics Covered
- Matrix theory foundations for deep learning
- Block matrix multiplications and tensor factorization
- Stochastic gradient descent and adaptive optimizers (Adam, RMSProp, etc.)
- Numerical stability in optimization: conditioning, exploding/vanishing gradients, preconditioning
- Second-order optimization algorithms: natural gradient, quasi-Newton, Fisher-based methods
- Probabilistic and information-theoretic tools: entropy, KL divergence, variational inference
- Model compression: quantization, structured/unstructured pruning, knowledge distillation
- Hardware-aware optimization: mixed-precision training, quantization-aware training
- Scaling laws and training dynamics of large AI models
- Evaluation metrics for efficiency: FLOPs, throughput, latency, memory footprint, and accuracy trade-offs
## Course Work
Students are expected to create an **online e-Book** synthesizing the material, with simplified explanations, references to videos, and interactive examples. Computational notebooks should be organized in a **public GitHub repository** for reproducibility.
## Expected Outcomes
After this course, students will be able to:
- Demonstrate mastery of matrix theory applied to deep learning
- Explain modern model compression techniques from a matrix theory perspective
- Understand advanced optimization algorithms, including second-order methods
- Apply probabilistic and information-theoretic concepts to optimization and compression
- Produce professional-quality documentation and computational notebooks
## Grading
- **100%** – Final e-Book report and GitHub deliverables
## Exams
- There are no exams; grades are based solely on reports and supplemental deliverables.
## Academic Conduct
Students are expected to comply with the **Student Code of Academic Conduct** regarding plagiarism and misconduct. More information: [Dean of Students – Academic Conduct](https://uwf.edu/deanofstudents)
## Minimum Technical Skills
Students should be able to:
- Activate a MyUWF student account
- Access MyUWF portal 2–3 times per week
- Access UWF email 2–3 times per week
- Perform basic word processing
Student use of technology is governed by the **Computing Resources Usage Agreement** and the **Student Communications Policy**.
## Course Modality
Faculty may adjust the modality of class meetings due to weather, pandemics, or other events. Flexibility is required to maintain continuity.
## TurnItIn
UWF maintains a license for **Turnitin** to check originality. Instructors may also use other methods as needed.
## AI Usage Policy
Generative AI tools are **permitted** for:
- Brainstorming ideas
- Finding information
- Drafting outlines
- Developing code snippets
- Grammar/style checks
Usage must be documented and cited. Unauthorized use may result in a zero.
## Discrimination, Harassment, and Civil Discourse
- Title IX compliance is required. Support services are available confidentially.
- Civil discourse is expected. Understanding ideas does not require agreement.
## Health, Safety, and Support Services
- **Student Health Clinic:** 850-474-2172
- **Counseling & Psychological Services:** 850-474-2420
- **TAO Online Self-Help Program:** Available 24/7
- **TogetherAll Peer Support:** [http://uwf.edu/togetherall](http://uwf.edu/togetherall)
- **ArgoWell Wellness Initiative:** [http://uwf.edu/argowell](http://uwf.edu/argowell)
- **Ask-a-Librarian Live Chat:** Available 8am–11pm Mon–Thu; 8am–4pm Fri; 9am–4pm Sat; 9am–11pm Sun
## Resources
1. **Cybersecurity library resources**
2. **Writing Lab:** Graduate/undergraduate assistants available for review
3. **Canvas Support Hotline:** 1-844-866-3349
**UWF ITS Help Desk:** 850-474-2075, itshelpdesk@uwf.edu
4. **Career Development & Community Engagement (CDCE):** Resume, cover letter, interview support
## Emergency Information
- University closures and alerts: [UWF Emergency Info](https://uwf.edu/emergency)
- Mobile Alert and WUWF-FM 88.1 MHz provide official updates
- Hurricane preparation procedures: [Emergency Procedures Guide](https://uwf.edu/emergency)
## Other Course Policies
- **Online Resources:** Use Discord for course discussions
- **Communication:** Contact instructor by appointment; check UWF email regularly
- **Class Attendance:** Meetings held face-to-face or online
- **Withdraw Policy:** Check UWF Academic Calendar; late withdrawals = WF
- **Incomplete Grades:** Only with extenuating circumstances and ≥70% completed work
> **Note:** Any syllabus changes during the semester take precedence.